U4SSC - Verification report - Narvik, Norway

U4SSC - Verification Report - TROMSØ, NORWAY

U4SSC - KPIs verification manual methodology for verifiers

U4SSC - Verification report - Valencia - Version 3

U4SSC - Verification report - Geneva, Switzerland

U4SSC - Verification report - Anyang, Korea (Republic of)

A new I-9 employment eligibility form allows opportunities for roofers to streamline operations and verify remotely

Tell Instagram your birthday or get blocked as app introduces age verification

El Segundo, CA United States - Job Description At Boeing, we innovate and collaborate to make the world a better place. From the seabed to outer space, you can contribute to work that matters with a company where diversity, equity and inclusion are shared values. We’re committed to fostering an... View

In this episode, Thomas Domville delves into the new feature introduced in iOS 17 that allows users to automatically delete one-time verification codes after they have been used. This feature is useful for keeping your Mail inbox and Messages app clutter-free.

To enable this feature:

1. Open Settings on your iPhone or iPad.
2. Scroll down and double-tap Passwords (you’ll be prompted to use Face ID or Touch ID to authenticate the next screen).
3. Double-tap Password Options, and toggle the "Clean Up Automatically" switch on.

Prelude is a new a new French startup that focuses on SMS verification; it’s coming out of stealth Wednesday. The two founders met when they were working for Zenly, a popular location-sharing app with tens of millions of users that was acquired by Snap (and later shut down). While you might not…

Artificial intelligence (AI) is everywhere. Machine learning (ML) and its associated inference abilities promise to revolutionize everything from driving your car to making your breakfast. Verification is never truly complete; it is over when you run...(read more)

Ensuring that the RTL designs correctly implement the C++ algorithmic intent in every circumstance is difficult to achieve with conventional verification. Learn more how Jasper C2RTL App helps to perform equivalence checking with 100x performance improvement(read more)

Verisium SimAI App harnesses the power of machine learning technology with the Cadence Xcelium Logic Simulator - the ultimate breakthrough in accelerating verification closure. It builds models from regressions run in the Xcelium simulator, enabling the generation of new regressions with specific targets. The Verisium SimAI app also features cousin bug hunting, a unique capability that uses information from difficult-to-hit failures to expose cousin bugs. With these advanced machine learning techniques, Verisium SimAI offers the potential for a significant boost in productivity, promising an exciting future for our users.

Figure 1: Regression compression and coverage maximization with Verisium SimAI 

What can I do with Verisium SimAI?

You can exercise different use cases with Verisium SimAI as per your requirements. For some users, the goal might be regression compression and improving coverage regain. Coverage maximization and hitting new bins could be another goal. Other users may be interested in exposing hard-to-hit failures, bug hunting for difficult to find issues. Verisium SimAI allows users to take on any of these challenges to achieve the desired results.

Let's go into some more details of these use cases and scenarios where using SimAI can have a big positive impact.

1. Using SimAI for Regression Compression and Coverage Regain

Unlock up to 10X compute savings with SimAI!

Verisium SimAI can be used to compress regressions and regain coverage. This flow involves setting up your regression environment for SimAI, running your random regressions with coverage and randomization data followed by training, and finally, synthesizing and running the SimAI-generated compressed regressions. The synthesized regression may prune tests that do not help meet the goal and add more runs for the most relevant tests, as well as add run-specific constraints. This flow can also be used to target specific areas like areas involving a high code churn or high complexity.

You can check out the details of this flow with illustrative examples in the following Rapid Adoption Kits (RAK) available on the Cadence Learning and Support Portal (Cadence customer credentials needed):

• Using SimAI with vManager (For Regression Compression and Coverage Regain) (RAK) 
• Using SimAI with a Generic Runner (For Regression Compression and Coverage Regain) (RAK)

2. Using SimAI for Coverage Maximization and Targeting coverage holes

Reduce your Functional Coverage Holes by up to 40% using SimAI!

Verisium SimAI can be used for iterative coverage maximization. This is most effective when regressions are largely saturated, and SimAI will explicitly try to hit uncovered bins, which may be hard-to-hit (but not impossible) coverage holes. This is achieved using iterative learning technology where with each iteration, SimAI does some exploration and determines how well it performed. This technique can also be used for bug hunting by using holes as targets of interest.

See more details on the Cadence Learning and Support Portal:

• Using SimAI for Coverage Maximization - vManager flow (RAK) 
• Using SimAI for Coverage Maximization - Generic Runner Flow (RAK)

3. Using SimAI for Bug Hunting

Discover and fix bugs faster using SimAI!

Verisium SimAI has a new bug hunting flow which can be used to target the goal of exposing hard-to-hit failure conditions. This is achieved using an iterative framework and by targeting failures or rare bins. The goal to target failures is best exercised when the overall failure rate is typically low (below 5%). Iterative learning can be used to improve the ability to target specific areas. Use the SimAI bug hunting use case to target rare events, low hit coverage bins, and low hit failure signatures.

See more details on the Cadence Learning and Support Portal:

• Using SimAI for Bug Hunting with vManager (RAK) 
• Using SimAI for Bug Hunting – Generic runner flow (RAK) 

Unlock compute savings, reduce your functional coverage holes, and discover and fix bugs faster with the power of machine learning technology now enabled by Verisium SimAI!

Please keep visiting  https://support.cadence.com/raks to download new RAKs as they become available.

Please note that you will need the Cadence customer credentials to log on to the Cadence Online  Support  https://support.cadence.com/, your 24/7 partner for getting help in resolving issues related to Cadence software or learning Cadence tools and technologies.

Happy Learning!

The course "Jasper Formal Fundamentals v24.03" introduces formal analysis to those who want to use formal analysis for design or verification. 

To optimally benefit from this course, you must already have sufficient knowledge of the System Verilog assertions to be capable of writing properties for formal verification. Hence, this training provides a module on formal analysis to help cover this essential background. 

In this course, you will learn how to code efficient SVA Properties for formal analysis, understand formal complexity and how to overcome it, and learn the basics of formal coverage.

After completing this course, you will be able to:

• Define reusable, functionally correct SVA properties that are efficient for formal tools. These shall use abstract auxiliary code to simplify descriptions, make code maintenance easier, reduce debug time, and reduce tool-proof runtime.
• Set up, run, and analyze results from formal analysis.
• Identify designs upon which formal is likely to be successful while understanding formal complexity issues and how to identify and overcome them.
• Use a systematic property development process to approach a completely new verification problem.
• Understand the basics of formal coverage.

 The most recently updated release includes new modules on:

• "Basic complexity handling" which discusses the complexity in formal and how to identify and handle them.
• "Complexity reduction methods” which discusses the complexity reduction methods and which is suitable for which type of complexity problem.
• “Coverage in formal” which discusses the basics of coverage in formal verification and how coverage can be used in formal.   

Take this course to learn the basics of formal verification. 

What's Next? 

You can check out the complete training: Jasper Formal Fundamentals. There is a free online version of the training available 24/7 for all customers with a Cadence Learning and Support Portal account. If you are interested in an instructor-led version of the training, please contact Cadence Training. And don't forget to obtain your digital badge after completing the training!

You can also check Jasper University page for more materials on formal analysis and Jasper apps. 

Related Trainings 

Jasper Formal Expert Training Course | Cadence

Verilog Language and Application Training Course | Cadence

SystemVerilog for Design and Verification Training Course | Cadence

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The application of real-time data processing or responsiveness is crucial, such as in high-performance computing, data centers, or applications requiring low-latency data transfers. It enables efficient use of PCIe bandwidth and resources by intelligently managing memory write operations based on system dynamics and workload priorities. By effectively leveraging Deferrable Memory Write [DMWr], Devices can achieve optimized performance and responsiveness, aligning with the evolving demands of modern computing applications.

What Is Deferrable Memory Write?

Deferrable Memory Write (DMWr) ECN introduced this new memory transaction type, which was later officially incorporated in PCIe 5.0 to CXL2.0. This enhanced type of memory transaction is Deferrable Memory Write [DMWr], which flows as another type of existing Read/Write memory transaction; the major difference of this Deferrable Memory Write, where the Requester attempts to write to a given location in Memory Space using the non-posted DMWr TLP Type, it Postponing their completion of memory write transactions to improve overall system efficiency and performance, those memory write operation can be delay or deferred until other priority task complete.

The Deferrable Memory Write (DMWr) requires the Completer to return an acknowledgment to the Requester and provides a mechanism for the recipient to defer (temporarily refuse to service) the Request.

DMWr provides a mechanism for Endpoints and hosts to choose to carry out or defer incoming DMWr Requests. This mechanism can be used by Endpoints and Hosts to simplify the design of flow control, reduce latency, and improve throughput. The Deferrable Memory writes TLP format in Figure A.

(Fig A) Deferrable Memory writes TLP format.

Example Scenario

Here's how the DMWr works with a simplified example: Imagine a system with an endpoint device (Device A) and a host CPU (Device B). Device B wants to write data to Device A's memory, but due to varying reasons such as system bus congestion or prioritization of other transactions, Device A can defer the completion of the memory write request. Just follow these steps:

1. Initiation of Memory Write: Device B initiates a memory write transaction to Device A. This involves sending the memory write request along with the data payload over the PCIe physical layer link.
2. Acknowledgment and Deferral: Upon receiving the memory write request, Device A acknowledges the transaction but may decide to defer its completion. Device A sends an acknowledgment (ACK) back to Device B, indicating it has received the data and intends to complete the write operation but not immediately.
3. Deferred Completion: Device A defers the completion of the memory write operation to a later, more opportune time. This deferral allows Device A to prioritize other transactions or optimize the use of system resources, such as memory bandwidth or processor availability.
4. Completion and Response: At a later point, Device A completes the deferred memory write operation and sends a completion indication back to Device B. This completion typically includes any status updates or additional information related to the transaction.

Usage or Importance of DMWr

Deferrable Memory Write usage provides the improvement in the following aspects:

• Reduced Latency: By deferring less critical memory write operations, more critical transactions can be processed with lower latency, improving overall system responsiveness.
• Improved Efficiency: Optimizes the utilization of system resources such as memory bandwidth and CPU cycles, enhancing the efficiency of data transfers within the PCIe architecture.
• Enhanced Performance: Allows devices to manage and prioritize transactions dynamically, potentially increasing overall system throughput and reducing contention.

Challenges in the Implementation of DMWr Transactions

The implementation of deferrable memory writes (DMWr) introduces several advancements and challenges in terms of usage and verification:

1. Timing and Synchronization: DMWr allows transactions to be deferred, complicating timing requirements or completing them within acceptable timing windows to avoid protocol violations. Ensuring proper synchronization between devices becomes critical to prevent data loss or corruption.
2. Protocol Compliance: Verification must ensure compliance with ECN PCIe 6.0 and CXL specifications regarding when and how DMWr transactions can be initiated and completed.
3. Performance Optimization: While DMWr can improve overall system performance by reducing latency, verifying its impact on system performance and ensuring it meets expected benchmarks is crucial.
4. Error Handling: Handling errors related to deferred transactions adds complexity. Verifying error detection and recovery mechanisms under various scenarios (e.g., timeout during deferral) is essential.

Verification Challenges of DMWr Transactions

The challenges to verifying the DMWr transaction consist of all checks with respect to Function, Timing, Protocol compliance, improvement, Error scenario, and security usage on purpose, as well as Data integrity at the PCIe and CXL.

1. Functional Verification: Verifying the correct implementation of DMWr at both ends of the PCIe link (transmitter and receiver) to ensure proper functionality and adherence to specifications.
2. Timing Verification: Validating timing constraints associated with deferring writes and ensuring transactions are completed within specified windows without violating protocol rules.
3. Protocol Compliance Verification: Checking that DMWr transactions adhere to PCIe and CXL protocol rules, including ordering rules and any restrictions on deferral based on the transaction type.
4. Performance Verification: Assessing the impact of DMWr on overall system performance, including latency reduction and bandwidth utilization, through simulation and testing.
5. Error Scenario Verification: Creating and testing scenarios to verify error handling mechanisms related to DMWr, such as timeouts, retries, and recovery procedures.
6. Security Considerations: Assessing potential security vulnerabilities related to DMWr, such as data integrity risks during deferred transactions or exposure to timing-based attacks.

Major verification challenges and approaches are timing and synchronization verification in the context of implementing deferrable memory writes (DMWr), which is crucial due to the inherent complexities introduced by deferred transactions. Here are the key issues and approaches to address them:

Timing and Synchronization Issues

1. Transaction Completion Timing:
   • Issue: Ensuring deferred transactions are completed within the specified time window without violating protocol timing constraints.
   • Approach: Design an internal timer and checker to model worst-case scenarios where transactions are deferred and verify that they are complete within allowable latency limits. This involves simulating various traffic loads and conditions to assess timing under different scenarios.
2. Ordering and Dependencies:
   • Issue: Verifying that transactions deferred using DMWr maintain the correct ordering and dependencies relative to non-deferred transactions.
   • Approach: Implement test scenarios that include mixed traffic of DMWr and non-DMWr transactions. Verify through simulation or emulation that dependencies and ordering requirements are correctly maintained across the PCIe link.
3. Interrupt Handling and Response Times:
   • Issue: Verify the handling of interrupts and ensure timely responses from devices involved in DMWr transactions.
   • Approach: Implement test cases that simulate interrupt generation during DMWr transactions. Measure and verify the response times to interrupts to ensure they meet system latency requirements.

In conclusion, while deferrable memory writes in PCIe and CXL offer significant performance benefits, their implementation and verification present several challenges related to timing, protocol compliance, performance optimization, and error handling. Addressing these challenges requires rigorous testing and testbench of traffic, advanced verification methodologies, and a thorough understanding of PCIe specifications and also the motivation behind introducing this Deferrable Write is effectively used in the CXL further. Outcomes of Deferrable Memory Write verify that the performance benefits of DMWr (reduced latency, improved throughput) are achieved without compromising timing integrity or violating protocol specifications.

In summary, PCIe and CXL are complex protocols with many verification challenges. You must understand many new Spec changes and consider the robust verification plan for the new features and backward compatible tests impacted by new features. Cadence's PCIe 6.0 Verification IP is fully compliant with the latest PCIe Express 6.0 specifications and provides an effective and efficient way to verify the components interfacing with the PCIe 6.0 interface. Cadence VIP for PCIe 6.0 provides exhaustive verification of PCIe-based IP and SoCs, and we are working with Early Adopter customers to speed up every verification stage.

More Information

• For more info on how Cadence PCIe Verification IP and TripleCheck VIP enable users to confidently verify PCIe 6.0, see our VIP for PCI Express, VIP for Compute Express Link, and TripleCheck for PCI Express
• See the PCI-SIG website for more details on PCIe in general and the different PCI standards.

Peripheral Component Interconnect Express (PCIe) is a high-speed interface standard widely used for connecting processors, memory, and peripherals. With the increasing reliance on PCIe to handle sensitive data and critical high-speed data transfer, ensuring data integrity and encryption during verification is the most essential goal. As we know, in the field of verification, randomization is a key technique that drives robust PCIe verification. It introduces unpredictability to simulate real-world conditions and uncover hidden bugs from the design. This blog examines the significance of randomization in PCIe IDE verification, focusing on how it ensures data integrity and encryption reliability, while also highlighting the unique challenges it presents. For more relevant details and understanding on PCIe IDE you can refer to Introducing PCIe's Integrity and Data Encryption Feature . The Importance of Data Integrity and Data Encryption in PCIe Devices Data Integrity : Ensures that the transmitted data arrives unchanged from source to destination. Even minor corruption in data packets can compromise system reliability, making integrity a critical aspect of PCIe verification. Data Encryption : Protects sensitive data from unauthorized access during transmission. Encryption in PCIe follows a standard to secure information while operating at high speeds. Maintaining both data integrity and data encryption at PCIe’s high-speed data transfer rate of 64GT/s in PCIe 6.0 and 128GT/s in PCIe 7.0 is essential for all end point devices. However, validating these mechanisms requires comprehensive testing and verification methodologies, which is where randomization plays a very crucial role. You can refer to Why IDE Security Technology for PCIe and CXL? for more details on this. Randomization in PCIe Verification Randomization refers to the generation of test scenarios with unpredictable inputs and conditions to expose corner cases. In PCIe verification, this technique helps us to ensure that all possible behaviors are tested, including rare or unexpected situations that could cause data corruption or encryption failures that may cause serious hindrances later. So, for PCIe IDE verification, we are considering the randomization that helps us verify behavior more efficiently. Randomization for Data Integrity Verification Here are some approaches of randomized verifications that mimic real-world traffic conditions, uncovering subtle integrity issues that might not surface in normal verification methods. 1. Randomized Packet Injection: This technique randomized data packets and injected into the communication stream between devices. Here we Inject random, malformed, or out-of-sequence packets into the PCIe link and mix valid and invalid IDE-encrypted packets to check the system’s ability to detect and reject unauthorized or invalid packets. Checking if encryption/decryption occurs correctly across packets. On verifying, we check if the system logs proper errors or alerts when encountering invalid packets. It ensures coverage of different data paths and robust protocol check. This technique helps assess the resilience of the IDE feature in PCIe in below terms: (i) Data corruption: Detecting if the system can maintain data integrity. (ii) Encryption failures: Testing the robustness of the encryption under random data injection. (iii) Packet ordering errors: Ensuring reordering does not affect data delivery. 2. Random Errors and Fault Injection: It involves simulating random bit flips, PCRC errors, or protocol violations to help validate the robustness of error detection and correction mechanisms of PCIe. These techniques help assess how well the PCIe IDE implementation: (i) Detects and responds to unexpected errors. (ii) Maintains secure communication under stress. (iii) Follows the PCIe error recovery and reporting mechanisms (AER – Advanced Error Reporting). (iv) Ensures encryption and decryption states stay synchronized across endpoints. 3. Traffic Pattern Randomization: Randomizing the sequence, size, and timing of data packets helps test how the device maintains data integrity under heavy, unpredictable traffic loads. Randomization for Data Encryption Verification Encryption adds complexity to verification, as encrypted data streams are not readable for traditional checks. Randomization becomes essential to test how encryption behaves under different scenarios. Randomization in data encryption verification ensures that vulnerabilities, such as key reuse or predictable patterns, are identified and mitigated. 1. Random Encryption Keys and Payloads: Randomly varying keys and payloads help validate the correctness of encryption without hardcoding assumptions. This ensures that encryption logic behaves correctly across all possible inputs. 2. Randomized Initialization Vectors (IVs): Many encryption protocols require a unique IV for each transaction. Randomized IVs ensure that encryption does not repeat patterns. To understand the IDE Key management flow, we can follow the below diagram that illustrates a detailed example key programming flow using the IDE_KM protocol. Figure 1: IDE_KM Example As Figure 1 shows, the functionality of the IDE_KM protocol involves Start of IDE_KM Session, Device Capability Discovery, Key Request from the Host, Key Programming to PCIe Device, and Key Acknowledgment. First, the Host starts the IDE_KM session by detecting the presence of the PCIe devices; if the device supports the IDE protocol, the system continues with the key programming process. Then a query occurs to discover the device’s encryption capabilities; it ensures whether the device supports dynamic key updates or static keys. Then the host sends a request to the Key Management Entity to obtain a key suitable for the devices. Once the key is obtained, the host programs the key into the IDE Controller on the PCIe endpoint. Both the host and the device now share the same key to encrypt and authenticate traffic. The device acknowledges that it has received and successfully installed the encryption key and the acknowledgment message is sent back to the host. Once both the host and the PCIe endpoint are configured with the key, a secure communication channel is established. From this point, all data transmitted over the PCIe link is encrypted to maintain confidentiality and integrity. IDE_KM plays a crucial role in distributing keys in a secure manner and maintaining encryption and integrity for PCIe transactions. This key programming flow ensures that a secure communication channel is established between the host and the PCIe device. Hence, the Randomized key approach ensures that the encryption does not repeat patterns. 3. Randomization PHE: Partial Header Encryption (PHE) is an additional mechanism added to Integrity and Data Encryption (IDE) in PCIe 6.0. PHE validation using a variety of traffic; incorporating randomization in APIs provided for validating PHE feature can add more robust Encryption to the data. Partial Header Encryption in Integrity and Data Encryption for PCIe has more detailed information on this. Figure 2: High-Level Flow for Partial Header Encryption 4. Randomization on IDE Address Association Register values: IDE Address Association Register 1/2/3 are supposed to be configured considering the memory address range of IDE partner ports. The fields of IDE address registers are split multiple values such as Memory Base Lower, Memory Limit Lower, Memory Base Upper, and Memory Limit Upper. IDE implementation can have multiple register blocks considering addresses with 32 or 64, different registers sizes, 0-255 selective streams, 0-15 address blocks, etc. This Randomization verification can help verify all the corner cases. Please refer to Figure 2. Figure 3: IDE Address Association Register 5. Random Faults During Encryption: Injecting random faults (e.g., dropped packets or timing mismatches) ensures the system can handle disruptions and prevent data leakage. Challenges of IDE Randomization and its Solution Randomization introduces a vast number of scenarios, making it computationally intensive to simulate every possibility. Constrained randomization limits random inputs to valid ranges while still covering edge cases. Again, using coverage-driven verification to ensure critical scenarios are tested without excessive redundancy. Verifying encrypted data with random inputs increases complexity. Encryption masks data, making it hard to verify outputs without compromising security. Here we can implement various IDE checks on the IDE callback to analyze encrypted traffic without decrypting it. Randomization can trigger unexpected failures, which are often difficult to reproduce. By using seed-based randomization, a specific seed generates a repeatable random sequence. This helps in reproducing and analyzing the behavior more precisely. Conclusion Randomization is a powerful technique in PCIe verification, ensuring robust validation of both data integrity and data encryption. It helps us to uncover subtle bugs and edge cases that a non-randomized testing might miss. In Cadence PCIe VIP, we support full-fledged IDE Verification with rigorous randomized verification that ensures data integrity. Robust and reliable encryption mechanisms ensure secure and efficient data communication. However, randomization also brings various challenges, and to overcome them we adopt a combination of constrained randomization, seed-based testing, and coverage-driven verification. As PCIe continues to evolve with higher speeds and focuses on high security demands, our Cadence PCIe VIP ensures it is in line with industry demand and verify high-performance systems that safeguard data in real-world environments with excellence. For more information, you can refer to Verification of Integrity and Data Encryption(IDE) for PCIe Devices and Industry's First Adopted VIP for PCIe 7.0 . More Information: For more info on how Cadence PCIe Verification IP and TripleCheck VIP enables users to confidently verify IDE, see our VIP for PCI Express , VIP for Compute Express Link for and TripleCheck for PCI Express For more information on PCIe in general, and on the various PCI standards, see the PCI-SIG website .

Hello,

I am new in formal verification and I have a concept question about how to verify an I2C Slave block.

I think the response should be valid for any serial interface which needs to receive information for several clocks before making an action.

The the protocol description is the following: 

I have a serial clock (SCL), Serial Data Input (SDI) and Serial Data Output (SDO), all are ports of the I2C Slave block.

The protocol looks like this:

The first byte which is received by the slave consists in 7bits of sensor address and the 8th bit is the command 0/1 Write/Read.

After the first 8 bits, the slave sends an ACK (SDO = 1 for 1 clock) if the sensor address is correct.

Lets consider only this case, where I want to verify that the slave responds with an ACK if the sensor address is correct.

The only solution I found so far was to use the internal buffer from the block which saves the received bits during 8 clocks. The signal is called shift_s.

I also needed to use internal chip state (state_s) and an internal counter (shift_count_s).

Instead of doing an direct check of the SDO(sdo_o) depending on SDI (sdi_d_i), I used the internal shift_s register.

My question is if my approach is the correct one or there is a possibility to write the verification at a blackbox level.

Below you have the 2 properties: first checks connection from SDI to internal buffer, the second checks the connection between internal buffer and output.

property prop_i2c_sdi_store;
  @(posedge sclk_n_i)
  $past(i2c_bl.state_s == `STATE_RECEIVE_I2C_ADDR)
    |-> i2c_bl.shift_s == byte'({ $past(i2c_bl.shift_s), $past(sdi_d_i)});
endproperty
APF_I2C_CHECK_SDI_STORE: assert property(prop_i2c_sdi_store);

property prop_i2c_sensor_addr(sens_addr_sel, sens_addr);
@(posedge sclk_n_i) (i2c_bl.state_s == `STATE_RECEIVE_I2C_ADDR) && (i2c_addr_i == sens_addr_sel) && (i2c_bl.shift_count_s == 7)
  ##1 (i2c_bl.shift_s inside {sens_addr, sens_addr+1}) |-> sdo_o;
endproperty
APF_I2C_CHECK_SENSOR_ADDR0: assert property(prop_i2c_sensor_addr(0, `I2C_SENSOR_ADDRESS_A0));
APF_I2C_CHECK_SENSOR_ADDR1: assert property(prop_i2c_sensor_addr(1, `I2C_SENSOR_ADDRESS_A1));
APF_I2C_CHECK_SENSOR_ADDR2: assert property(prop_i2c_sensor_addr(2, `I2C_SENSOR_ADDRESS_A2));
APF_I2C_CHECK_SENSOR_ADDR3: assert property(prop_i2c_sensor_addr(3, `I2C_SENSOR_ADDRESS_A3));

PS: i2c_addr_i is address selection for the slave (there are 4 configurable sensor addresses, but this is not important for the case).

Thank you!

Read this blog to know how you can easily create and insert connect modules using Spectre AMS Designer with the Verilog-AMS standard language defined by Accellera. (read more)

Have you ever wanted to write a DRC rule deck to check for space or width constraints on polygons? Or have you wondered how the multiple lines of an LVS rule deck extract and conduct a comparison between the schematic and layout? Maybe you've been curious about the role of rule deck writers in creating high-quality designs ready for tape-out.

If any of these questions interest you, there is good news: the latest version (v23.1) of the Physical Verification Rules Writer (PVLRW) course is designed to teach you rule deck writing. This free 16-hour online course includes audio and labs designed to make your learning experience comfortable and flexible. Whether you are new to the concept or an experienced CAD/PDK engineer, the course is structured to enhance your rule deck writing skills.

The PVLRW course covers six core modules: Layer Processing, DRC Rules, Layout Extraction, ERC and LVS Rules, Schematic Netlisting, and Coloring Rules. There are also three optional appendix sections. Each module explains relevant rules with syntax, concepts, graphics, examples, and case studies.

This course is based on tool versions PEGASUS231 and Virtuoso Studio IC231.

Pegasus Input and Output

Pegasus is a cloud-ready physical verification signoff solution that enables engineers to support faster delivery of advanced-node integrated circuits (ICs) to market.

Pegasus requires input data in the form of layout geometry, schematic netlists, and rules that direct the tool operation. The rules fall into two categories: those that describe the fabrication process and those that control the job-specific operation.

Pegasus provides log and report files, netlists, databases, and error databases as output.

Overview of Pegasus Rule File

The rule decks written in Physical Verification Language (PVL) work for the Cadence PV signoff tools Pegasus and PVS (Physical Verification System).   

The PVL rules are placed in a file that gets selected in a run from the GUI or the command line, as the user directs. PVL rules may be on separate lines within the file and can also be contained in named rule blocks.

Each line of code starts with a PVL rule that uses prefix type notation. It consists of a keyword followed by options, input layer or variable names, and output layer or variable names.

A rule block has the format of the keyword rule, followed by a rule name you wish to give it, followed by an opening curly brace. You enter the rules you wish to perform, followed by a closing curly brace on the last separate line.

  Sample Rule deck with individual lines of code and rule blocks.

DRC Rules

The first step in a typical Pegasus flow is a Design Rule Check (DRC), which verifies that layout geometries conform to the minimum width, spacing, and other fabrication process rules required by an IC foundry. Each foundry specifies its own process-dependent rules that must be met by the layout design.

There are three types of DRC rules: layer definition rules, layer derivation rules, and DRC design check rules. Layer definition rules identify the layers contained in the input layout database, and layer derivation rules derive additional layers from the original input layers, allowing the tool to test the design against specific foundry requirements using the design check rules.

A sample DRC Rule deck

A layout view displaying the DRC violations

LVS Rules

The Pegasus Layout Versus Schematic (LVS) tool compares the layout netlist with the schematic netlist to check for discrepancies.

There are two essential LVS rule sets: LVS extraction rules and comparison rules. LVS extraction rules help extract drawn devices and connectivity information from the input layout geometry data and outputs into a layout netlist. The LVS extraction rule set also includes the layer definition, derivation, extraction, connectivity, and net listing rules.

LVS comparison rules are associated with comparing the extracted layout netlist to a schematic netlist.

A sample LVS Rule deck. 

TCL, Macros, and Conditional commands

Tcl is supported and used in various Pegasus functionalities, such as Pegasus rule files and Pegasus configurator. Macros are functional templates that are defined once and can be used multiple times in a rule file. Conditional Commands are used to process or skip specific commands in the rule file.

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• If you need help with registration, contact support@cadence.com.

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About Knowledge Booster Training Bytes

Knowledge Booster Training Bytes is an online journal that relays information about Cadence Training videos, online courses, and upcoming webinars in the Learning section of the Cadence Learning and Support portal. This blog category brings you direct links to these videos, courses, and other related material on a regular basis. Subscribe to receive email notifications about our latest Custom IC Design blog posts.

Read this blog for an overview to the Circuit physical verification and parasitic extraction design stage in the Custom IC Design methodology and the key design steps which can help you achieve this.(read more)

81134a performance verification user guide

Apr 1, 2024

Report by Trevor Findlay about recent politics surrounding the Iranian Nuclear Program.

The National Testing Agency (NTA) has introduced new guidelines to help students avoid common issues with Aadhaar Card name mismatches when applying for the Joint Entrance Examination (JEE) Main 2025. Many applicants have been running into problems because the name on

Meta’s Threads app, seen as a formidable competitor to Twitter, has garnered over 10 million sign-ups within seven hours of launch, offering features similar to Twitter and attracting users from Instagram.

National Bureau of Economic Research

IEEE Computer Society

IEEE Computer Society

The move assumes significance as the commission last month cancelled the provisional candidature of probationary IAS officer Puja Khedkar

A focus on customer enablement across all Cadence sub-organizations has led to a cross-functional effort to identify opportunities to bring our customers to proficiency with our products and flows. Hence, Rapid Adoption Kits -- RAKs -- for Synthesis...(read more)

Cadence Encounter® Test uses breakthrough timing-aware and power-aware technologies to enable customers to manufacture higher quality, power-efficient silicon faster and at lower cost. Encounter Diagnostics identifies critical yield-limiting issues and...(read more)

The structural and nonlinear optical properties of a new anilinium hybrid crystal of chemical formula (C6H7NCl+·NO3−)3 have been investigated. The crystal structure was determined from single-crystal X-ray diffraction measurements performed at a temperature of 100 K which show that the compound crystallizes in a noncentrosymmetric space group (Pna21). The structural analysis was coupled with Hirshfeld surface analysis to evaluate the contribution of the different intermolecular interactions to the formation of supramolecular assemblies in the solid state that exhibit nonlinear optical features. This analysis reveals that the studied compound is characterized by a three-dimensional network of hydrogen bonds and the main contributions are provided by the O...H, C...H, H...H and Cl...H interactions, which alone represent ∼85% of the total contributions to the Hirshfeld surfaces. It is noteworthy that the halogen...H contributions are quite comparable with those of the H...H contacts. The nonlinear optical properties were investigated by nonlinear diffuse femtosecond-pulse reflectometry and the obtained results were compared with those of the reference material LiNbO3. The hybrid crystals exhibit notable second (SHG) and third (THG) harmonic generation which confirms its polarity is generated by the different intermolecular interactions. These measurements also highlight that the THG signal of the new anilinium compound normalized to its SHG counterpart is more pronounced than for LiNbO3.

Portable Barcode Verification System(LVS-9585 Series)

Portable Barcode Verification System(LVS-9580 Series)

Desktop Barcode Verification System(LVS-9510 Series)

The company saves valuable time and overhead costs; speeds patient access to needed healthcare services with outsources pre-certification/pre-authorization services.

Recent advances in deep learning have facilitated the design of speaker verification systems that directly input raw waveforms. For example, RawNet extracts speaker embeddings from raw waveforms, which simplifies the process pipeline and demonstrates competitive performance. In this study, we improve RawNet by scaling feature maps using various methods. The proposed mechanism utilizes a scale vector that adopts a sigmoid non-linear function. It refers to a vector with dimensionality equal to the number of filters in a given feature map. Using a scale vector, we propose to scale the feature map multiplicatively, additively, or both. In addition, we investigate replacing the first convolution layer with the sinc-convolution layer of SincNet. Experiments performed on the VoxCeleb1 evaluation dataset demonstrate the effectiveness of the proposed methods, and the best performing system reduces the equal error rate by half compared to the original RawNet. Expanded evaluation results obtained using the VoxCeleb1-E and VoxCeleb-H protocols marginally outperform existing state-of-the-art systems.

We present a new system, EEV, for verifying binarized neural networks (BNNs). We formulate BNN verification as a Boolean satisfiability problem (SAT) with reified cardinality constraints of the form $y = (x_1 + cdots + x_n le b)$, where $x_i$ and $y$ are Boolean variables possibly with negation and $b$ is an integer constant. We also identify two properties, specifically balanced weight sparsity and lower cardinality bounds, that reduce the verification complexity of BNNs. EEV contains both a SAT solver enhanced to handle reified cardinality constraints natively and novel training strategies designed to reduce verification complexity by delivering networks with improved sparsity properties and cardinality bounds. We demonstrate the effectiveness of EEV by presenting the first exact verification results for $ell_{infty}$-bounded adversarial robustness of nontrivial convolutional BNNs on the MNIST and CIFAR10 datasets. Our results also show that, depending on the dataset and network architecture, our techniques verify BNNs between a factor of ten to ten thousand times faster than the best previous exact verification techniques for either binarized or real-valued networks.

Most studies on speaker verification systems focus on long-duration utterances, which are composed of sufficient phonetic information. However, the performances of these systems are known to degrade when short-duration utterances are inputted due to the lack of phonetic information as compared to the long utterances. In this paper, we propose a method that compensates for the performance degradation of speaker verification for short utterances, referred to as "crop aggregating". The proposed method adopts an ensemble-based design to improve the stability and accuracy of speaker verification systems. The proposed method segments an input utterance into several short utterances and then aggregates the segment embeddings extracted from the segmented inputs to compose a speaker embedding. Then, this method simultaneously trains the segment embeddings and the aggregated speaker embedding. In addition, we also modified the teacher-student learning method for the proposed method. Experimental results on different input duration using the VoxCeleb1 test set demonstrate that the proposed technique improves speaker verification performance by about 45.37% relatively compared to the baseline system with 1-second test utterance condition.

A processing device comprises a processor coupled to a memory and implements an obligation management system for information technology infrastructure, with the obligation management system being configured to process a plurality of obligations on behalf of a relying party to verify implementation of corresponding controls in information technology infrastructure of a claimant. A given one of the obligations has an associated obligation fulfiller that is inserted or otherwise deployed as a component within the information technology infrastructure of the claimant and is configured to provide evidence of the implementation of one or more of the controls responsive to an obligation assertion so as to establish an associated trust aspect of the claimant. The information technology infrastructure may comprise distributed virtual infrastructure of a cloud service provider. The claimant may comprise the cloud service provider and the relying party may comprise a tenant of the cloud service provider.

A processor receives an instruction operation (OP) code from a verification system. The instruction OP code includes instruction bits and forced event bits. The processor identifies a forced event based upon the forced event bits, which is unrelated to an instruction that corresponds to the instruction bits. In turn, the processor executes the forced event.

A method of applying formal verification methodologies to event processing applications is provided herein. The method includes the following stages: representing an event processing application as an event processing network, being a graph with event processing agents as nodes; generating a finite state machine based on the event processing network, wherein the finite state machine is an over-approximation of the event processing application; expressing stateful rules and policies that are associated with the event processing application using temporal logic, to yield a temporal representation of the event processing application; combining the temporal representation and the finite state machine into a model; generating a statement associated with a user-selected verification-related property of the event processing application, wherein the statement is generated using the temporal representation; and applying the statement to the model, to yield an indication for: (i) a correctness of the statement or (ii) a counter example, respectively.

A plurality of Field Programmable Gate Arrays (FPGA), high performance transceivers, and memory devices provide a verification module for timing and state debugging of electronic circuit designs. Signal value compression circuits and gigabit transceivers embedded in each FPGA increase the fanout of each FPGA. Ethernet communication ports enable remote software debugging of processor instructions.

A method, a system and a computer readable medium for providing information relating to a verification of a digital circuit. The verification may be formal verification and comprise formally verifying that a plurality of formal properties is valid for a representation of the digital circuit. The method comprises replacing at least a first input value relating to the representation of the digital circuit by a first free variable, determining if at least one of the plurality of formal properties is valid or invalid after replacing the first input value by the first variable and indicating if the at least one of the plurality of formal property is valid or invalid. The use of a free or open variable that has not determined value can be directly in the description or representation of the digital circuit. It is not necessary to insert errors or to apply an error model.

Methods are provided to facilitate automated creation and management of design rule checking or DRC waiver descriptions. Embodiments include receiving a plurality of first checksums corresponding to respective first geometric element violations waived in association with a block of an integrated circuit design, the first checksums being based on a first version of at least one design verification rule and/or of the block, receiving a second checksum corresponding to a second geometric element violation associated with the block, the second checksum being based on a second version of the design verification rule and/or of the block, determining whether the second checksum corresponds to at least one of the first checksums, and, if the second checksum does not correspond to at least one first checksum, generating a waiver request for the second geometric element error.

A system, method, and computer program product for containing analog verification IP for circuit simulation. Embodiments introduce analog verification units (“vunits”), and corresponding analog verification files to contain them. Vunits allow circuit design verification requirement specification via text file. No editing of netlist files containing design IP is required to implement static and dynamic circuit checks, PSL assertions, clock statements, or legacy assertions. Vunits reference a top-level circuit or subcircuits (by name or by specific instance), and the simulator automatically binds vunit contents appropriately during circuit hierarchy expansion. Vunits may be re-used for other design cells, and may be easily processed by text-based design tools. Vunits may be provided via vunit_include statements in a control netlist file, command line arguments, or by directly placing a vunit block into a netlist. Vunits may also contain instance statements to monitor or process signals, such as those needed by assertions.

A technique for determining whether an integrated circuit design is susceptible to glitches includes identifying storage elements in an original register-transfer level (RTL) file of the integrated circuit design and identifying clock signals for each of the storage elements in the original RTL file. The technique also includes generating respective assertions for each of the identified clock signals and identifying potential glitchy logic in respective clock paths for each of the identified clock signals. Finally, the technique includes inserting, at the potential glitchy logic, glitches in each of the respective clock paths of the original RTL file to provide a modified RTL file and executing an RTL simulation using the modified RTL file and the respective assertions.