Wind River, an IoT and industrial operating system owned by Intel will be acquired by TPG, global alternative asset firm. Terms of the deal were not disclosed. Intel had bought Wind River Systems for $884 million in 2009

Wind River operates in several markets, including aerospace and defense, automotive, industrial, medical and networking technologies. Its core products in these markets are operating systems, software infrastructure platforms, device management, and simulation software. The IoT practice of Wind River provides consulting services for customers building IoT applications.

In a statement for Wind River, Nehal Raj, Partner and Head of Technology investing at TPG said “We see a tremendous market opportunity in industrial software driven by the convergence of the Internet of Things (IoT), intelligent devices and edge computing. As a market leader with a strong product portfolio, Wind River is well positioned to benefit from these trends. We are excited about the prospects for Wind River as an independent company, and plan to build on its strong foundation with investments in both organic and inorganic growth.”

Wind River’s main IoT product is Helix Device Cloud, a cloud-offering capable of managing deployed IoT devices and industrial equipment across a machine’s lifecycle. Helix can connect and manage devices remotely.

Helix platform’s key uses cases are gateway management, proactive maintenance, security updates, and device provisioning.

[SAPS] - Five suspects are appearing in the Kariega Magistrate's Court today, after they were arrested and found in possession of cycads with an estimated value of R1 Million on Friday 08 November 2024.

[SAnews.gov.za] Twenty-three people in Gauteng have died as a result of food borne-related poisoning after consuming food from spaza shops.

In the rapidly evolving landscape of automotive electronics, traditional monolithic design approaches are giving way to something more flexible and powerful—chiplets. These modular microchips, which are themselves parts of a whole silicon system, offer unparalleled potential for improving system performance, reducing manufacturing costs, and accelerating time-to-market in the automotive sector. However, the transition to working with chiplets in automotive electronics is not without its challenges.

Designers must now grapple with a new set of considerations, such as die-to-die interconnect standards, complex processes, and the integration of diverse IPs. Advanced toolsets and standardized design approaches are required to meet these challenges head-on and elevate the potential of chiplets in automotive innovation. In the following discourse, we will explore in detail the significance of chiplets in the context of automotive electronics, the obstacles designers face when working with this paradigm, and how Cadence comprehensive suite of IPs, tools, and flows is pioneering solutions to streamline the chiplet design process.

Unveiling Chiplets in Automotive Electronics

For automotive electronics, chiplets offer a methodology to modularize complex functionalities, integrate different chiplets into a package, and significantly enhance scalability and manufacturability. By breaking down semiconductor designs into a collection of chiplets, each fulfilling specific functions, automotive manufacturers can mix and match chiplets to rapidly prototype new designs, update existing ones, and specialize for the myriad of use cases found in vehicles today.

The increasing significance of chiplets in automotive electronics comes as a response to several industry-impacting phenomena. The most obvious among these is the physical restriction of Moore's Law, as large die sizes lead to poor yields and escalating production costs. Chiplets with localized process specialization can offer superior functionality at a more digestible cost, maintaining a growth trajectory where monolithic designs cannot. Furthermore, chiplets support the assembly of disparate technologies onto a single subsystem, providing a comprehensive yet adaptive solution to the diverse demands present in modern vehicles, such as central computing units, advanced driver-assistance systems (ADAS), infotainment units, and in-vehicle networks. This chiplet-based approach to functional integration in automotive electronics necessitates intricate design, optimization, and validation strategies across multiple domains.

The Complexity Within Chiplets

Yet, with the promise of chiplets comes a series of intricate design challenges. Chiplets necessitate working across multiple substrates and technologies, rendering the once-familiar 2-dimensional design space into the complex reality of multi-layered, sometimes even three-dimensional domains. The intricacies embedded within this design modality mandate devoting considerable attention to partitioning trade-offs, signal integrity across multiple substrates, thermal behavior of stacked dies, and the emergence of new assembly design kits to complement process design kits (PDKs).

To effectively address these complexities, designers must wield sophisticated tools that facilitate co-design, co-analysis, and the creation of a robust virtual platform for architectural exploration. Standardizations like the Universal Chip Interconnect Express (UCIe) have been influential, providing a die-to-die interconnect foundation for chiplets that is both standardized and automotive-ready. The availability of UCIe PHY and controller IP from Cadence and other leading developers further eases the integration of chiplets in automotive designs.

The Role of Foundries and Packaging in Chiplets

Foundries have also pivoted their services to become a vital part of the chiplet process, providing specialized design kits that cater to the unique requirements of chiplets. In tandem, packaging has morphed from being a mere logistical afterthought to a value-added aspect of chiplets. Organizations now look to packaging to deliver enhanced performance, reduced power consumption, and the integrity required by the diverse range of technologies encompassed in a single chip or package. This shift requires advanced multiscale design and analysis strategies that resonate across a spectrum of design domains.

Tooling Up for Chiplets with Cadence

Cadence exemplifies the rise of comprehensive tooling and workflows to facilitate chiplet-based automotive electronics design. Their integrations address the challenges that chiplet-based SoCs present, ensuring a seamless design process from the initial concept to production. The Cadence suite of tools is tailored to work across design domains, ensuring coherence and efficiency at every step of the chiplet integration process.

For instance, Cadence Virtuoso RF subflows have become critical in navigating radio frequency (RF) challenges within the chiplets, while tools such as the Integrity 3D-IC Platform and the Allegro Advanced Multi-Die Package Design Solution have surfaced to enable comprehensive multi-die package designs. The Integrity Signal Planner extends its capabilities into the chiplet ecosystem, providing a centralized platform where system-wide signal integrity can be proactively managed. Sigrity and Celsius, on the other hand, offer universally applicable solutions that take on the challenges of chiplets in signal integrity and thermal considerations, irrespective of the design domain. Each of these integrated analysis solutions underscores the intricate symphony between technology, design, and packaging essential in unlocking the potential of chiplets for automotive electronics.

Cadence portfolio includes solutions for system analysis, optimization, and signoff to complement these domain-specific tools, ensuring that the challenges of chiplet designs don't halt progress toward innovative automotive electronics. Cadence enables designers to engage in power- and thermal-aware design practices through their toolset, a necessity as automotive systems become increasingly sophisticated and power-efficient.

A Standardized Approach to Success with Chiplets

Cadence’s support for UCIe underscores the criticality of standardized approaches for heterogeneous integration by conforming to UCIe standards, which numerous industry stakeholders back. By co-chairing the UCIe Automotive working group, Cadence ensures that automotive designs have a universal and standardized Die-to-Die (D2D) high-speed interface through which chiplets can intercommunicate, unleashing the true potential of modular design.

Furthermore, Cadence champions the utilization of virtual platforms by providing transaction-level models (TLMs) for their UCIe D2D IP to simulate the interaction between chiplets at a higher level of abstraction. Moreover, individual chiplets can be simulated within a chiplet-based SoC context leveraging virtual platforms. Utilizing UVM or SCE-MI methodologies, TLMs, and virtual platforms serve as first lines of defense in identifying and addressing issues early in the design process before physical silicon even enters the picture.

Navigating With the Right Tools

The road to chiplet-driven automotive electronics is one paved with complexity, but with a commitment to standards, it is a path that promises significant rewards. By leveraging Cadence UCIe Design and Verification IP, tools, and methodologies, automotive designers are empowered to chart a course toward chiplets and help to establish a chiplet ecosystem. With challenges ranging from die-to-die interconnect to standardization, heterogeneous integration, and advanced packaging, the need for a seamless integrated flow and highly automated design approaches has never been more apparent. Companies like Cadence are tackling these challenges, providing the key technology for automotive designers seeking to utilize chiplets for the next-generation E/E architecture of vehicular technology.

In summary, chiplets have the potential to revolutionize the automotive electronics industry, breathing new life into the way vehicles are designed, manufactured, and operated. By understanding the significance of chiplets and addressing the challenges they present, automotive electronics is poised for a paradigm shift—one that combines the art of human ingenuity with the power of modular and scalable microchips to shape a future that is not only efficient but truly intelligent.

Learn more about how Cadence can help to enable automakers and OEMs with various aspects of automotive design.

The automotive industry is currently on the cusp of a radical evolution, steering towards a future where cars are not just vehicles but sophisticated, software-defined vehicles (SDV). This shift is marked by an increased reliance on automation and a significant increase in the use of sensors to improve safety and reliability. However, the increasing number of sensors has led to higher compute demands and poses challenges in managing a wide variety of data. The traditional method of using separate processors to manage each sensor's data is becoming obsolete. The current trends necessitate a unified processing system that can deal with multimodal sensor data, utilizing traditional Digital Signal Processing (DSP) and AI-driven algorithms. This approach allows for more efficient and reliable sensor fusion, significantly enhancing vehicle perception. Developers often face difficulties adhering to stringent power, performance, area, and cost (PPAC) and timing constraints while designing automotive SoCs.

Cadence, with its groundbreaking products and AI-powered processors, is enabling designers and automotive manufacturers to meet the future sensor fusion demands within the automotive sector. At the recent CadenceLive Silicon Valley 2024, Amol Borkar, product marketing director at Cadence, showcased the company's dedication and forward-thinking solutions in a captivating presentation titled "Addressing Tomorrow’s Sensor Fusion Needs in Automotive Computing with Cadence." This blog aims to encapsulate the pivotal takeaways from the presentation. If you missed the chance to watch this presentation live, please click here to watch it.

Significant Trends in the Automotive Market – Industry Landscape

We are witnessing a revolution in automotive technology. Innovations like occupant and driver monitoring systems (OMS, DMS), 4D radar imaging, LiDAR technology, and 360-degree view are pushing the boundaries of what's possible, leading us into an era of remarkable autonomy levels—ranging from no feet or hands required to eventually no eyes needed on the road.

Sensor Fusion and Increasing Processing Demands—Sensor fusion effectively integrates data from different sensors to help vehicles understand their surroundings better. Its main benefit is in overcoming the limitations of individual sensors. For example, cameras provide detailed visual information but struggle in low-light or lousy weather. On the other hand, radar is excellent at detecting objects in these conditions but lacks the detail that cameras provide. By combining the data from multiple sensors, automotive computing can take advantage of their strengths while compensating for their weaknesses, resulting in a more reliable and robust system overall.

One thing to note is that the increased number of sensors produces various data types, leading to more pre-processing.

On-Device Processing—As the industry moves towards autonomy, there is an increasing need for on-device data processing instead of cloud computing to enable vehicles to make informed decisions. Embracing on-device processing is a significant advancement for facilitating real-time decisions and avoiding round-trip latency.

AI Adoption—AI has become integral to automotive applications, driving safety, efficiency, and user experience advancements. AI models offer superior performance and adaptability, making future-proofing a crucial consideration for automotive manufacturers. AI significantly enhances sensor fusion algorithms, offering scalability and adaptability beyond traditional rule-based approaches. Neural networks enable various fusion techniques, such as early fusion, late fusion, and mid-fusion, to optimize the integration and processing of sensor data.

Future Sensor Fusion Needs

Automotive architectures are continually evolving. With current trends and AI integration into radar and sensor fusion applications, SoCs should be modular, flexible, and programmable to meet market demands.

Heterogeneous Architecture- Today's vehicles are loaded with various sensors, each with a unique processing requirement. Running the application on the most suitable processor is essential to achieve the best PPA. To meet such requirements, modern automotive solutions require a heterogeneous compute approach, integrating domain-specific digital signal processors (DSPs), neural processing units (NPUs), central processing unit (CPU) clusters, graphics processing unit (GPU) clusters, and hardware accelerator blocks. A balanced heterogeneous architecture gives the best PPA solution.

Flexibility and Programmability- The industry has come a long way from using computer vision algorithms such as HOG (Histogram Oriented Gradient) to detect people and objects, HAR classifier to detect faces, etc., to CNN and LSTM-based AI to Transformer models and graphical neural networks (GNN). AI has evolved tremendously over the last ten years and continues to evolve. To keep up with the evolving rate of AI, SoC design must be flexible and programmable for updates if needed in the future.

Addressing the Sensor Fusion Needs with Cadence

Cadence offers a complete suite of hardware and software products to address the increasing compute requirements in automotive. The comprehensive portfolio of Tensilica products built on the robust 32-bit RISC architecture caters to various automotive CPU and AI needs. What makes them particularly appealing is their scalability, flexibility, and configurability, offering many options to meet diverse needs.

The Xtensa family of products offers high-quality, power-efficient CPUs. Tensilica family also includes AI processors like Neo NPUs for the best power, performance, and area (PPA) for AI inference on devices or more extensive applications. Cadence also offers domain-specific products for DSPs such as HIFI DSPs, specialized DSPs and accelerators for radar and vision-based processing, and a general-purpose family of products for floating point applications.

The ConnX family offers a wide range of DSPs, from compact and low-power to high-performance, optimized for radar, lidar, and communications applications in ADAS, autonomous driving, V2X, 5G/LTE/4G, wireless communications, drones, and robotics. Tensilica's ISO26262 certification ensures compliance with automotive safety standards, making it a trusted partner for advanced automotive solutions. The Cadence NeuroWeave Software Development Kit (SDK) provides customers with a uniform, scalable, and configurable ML interface and tooling that significantly improves time to market and better prepares them for a continuously evolving AI market. Cadence Tensilica offers an entire ecosystem of software frameworks and compilers for all programming styles.

Tensilica's comprehensive software stack supports programming for DSPs, NPUs, and accelerators using C++, OpenCL, Halide, and various neural network approaches. Middleware libraries facilitate applications such as SLAM, radar processing, and Eigen libraries, providing robust support for automotive software development.

Conclusion

Cadence’s Tensilica products offer a development toolchain and various IPs tailored for the automotive industry, covering audio, vision, radar, unified DSPs, and NPUs. With ISO certification and a robust partner ecosystem, Tensilica solutions are designed to meet the future needs of automotive computing, ensuring safety, efficiency, and innovation.

Learn More

• Cadence Automotive Solutions
• Cadence Automotive IP
• Sensor Fusion and ADAS in TSMC Automotive Processes
• Revolution on the Road: How Cadence is Driving the Future of Automotive Design!
• Taming Design Complexity in Chiplet-Based Automotive Electronics
• UCIe and Automotive Electronics: Pioneering the Chiplet Revolution

Hello,

I was looking to destructively and efficiently modify a list that was passed in as an argument to a procedure, by prepending an item to the list.

I noticed that cons lets you do this efficiently, but the operation is non-destructive. Hence this wouldn't work if you are trying to modify a function's list parameter in place.

Here is an example of trying to add "0" to the front of a list:

A training webinar on Microwave Office will be given June 27, 2023. The emphasis will be on EM simulation.(read more)

The automotive industry revolutionized the definition of a vehicle in terms of safety, comfort, enhanced autonomy, and internet connectivity. With this trend, the automotive industry rapidly adopted automotive Ethernet such as 10Base-T1, 100Base-T1, and in some cases, 1000Base-T1. 

Faster Speed (than CAN-FD), Scalability, embedded security protocols (like MacSec), cost and energy efficiency, and simple yet redundant network made Ethernet an obvious choice over CAN(FD) and FlexRay.  

Ethernet 10Base-T1 

10BASE-T1S is defined under IEEE with 802.3cg. The S in 10BASE-T1S stands for a short distance. 10BASE-T1S uses a multidrop topology, where each node connects to a single cable. Multidrop topology eliminates the need for switches and, as a result, fewer cables/less cost. The primary goal of 10BASE-T1S is a deterministic transmission on a collision-free multidrop network. 10BASE-T1S cables use a pair of twisted wires. As per IEEE, at least eight nodes can connect to each, but more connections are feasible.    

The Physical Layer Collision Avoidance [PLCA] protocol ensures that it uses the entire 10 Mbps bandwidth. In 10BaseTs, Reconciliation Sublayer provides optional Physical Layer Collision Avoidance (PLCA) capabilities among participating stations. Using PLCA-enabled Physical Layers in CSMA/CD half-duplex shared-medium networks can provide enhanced bandwidth and improved access latency under heavily loaded traffic conditions. The working principle of PLCA is that transmit opportunities on a mixing segment are granted in sequence based on a node ID unique to the local collision domain (set by the management entity). 10BASE-T1S also supports an arbitration scheme that guarantees consistent node access to the media within a predefined time.  

The 10BASE-T1S PHY is intended to cover the low-speed/low-cost applications in the industrial and automotive environment. A large number of pins (16) required by the MII interface is one of the significant cost factors that must be addressed to fulfill this objective. The 10BASE-T1S "Transceiver" solution is suited for embedded systems where the digital portion of the PHY is fully integrated, e.g., into an MCU or an Ethernet switch core, leaving only the analog portion (the transceiver) into a separate IC. 

Ethernet 100Base-T1/1000Base-T1 

100Base-T1 and 1000Base-T1 can be used for audio/video information. With Higher bandwidth capacity, 100Base-T1/ 1000Base-T1 paired with AVB (Audio video bridging) can be used for car infotainment systems. 100Base-T1/1000Base-T1 paired with time-sensitive networking [TSN] protocol can be used to fulfill the automotive industry's mission-critical, time-sensitive, and deterministic latency needs. 

 PTP Over MacSec  

With today's automotive network, all the Electronic Control Units connected require timing accuracy and network synchronization, Precision Time Protocol (PTP), defined in IEEE 1588, provides synchronized clocks throughout a network.  While maintaining the timing accuracy for mission-critical applications, protecting the vehicle network from vulnerable threats is mandatory, and PTP over MacSec provides the consolidated solution.  

With the availability of the Cadence Verification IP for 10/100/1000BaseT1 and TSN, adopters can start working with these specifications immediately, ensuring compliance with the standard and achieving the fastest path to IP and SoC verification closure. The 10/100/1000GBaseT1 and TSN provide a full-stack solution, including support to the PHY, MAC, and TSN layers with a comprehensive coverage model and protocol checkers. Ethernet BaseT1 and TSN VIP covers all features required for complete coverage verification closure. More details are available in the Ethernet Verification IP portfolio. 

Krunal 

Live Doc is an advanced automated PCB documentation generation tool integrated with OrCAD X Presto designed to streamline the creation of PCB documentation. By automating the generation of PCB fabrication and assembly drawings, Live Doc significantly...(read more)

Does anyone know how to not include a pbar in a constraint manager analysis? I have some relative delay constraints applied on a group of differential nets. When I analyze the design these all show an error. If I delete the plating bar from the design they are all passing. The plating bar gets generated on the Substrate Geometry / Plating_Bar class. I understand that I could just delete the plating bar to verify the constraint but the issue is when I archive this design I would like it to be clean meaning it is in the final state for manufacturing AND passing all constraints according to design reviews.

Anyone have an idea? 

Thank you!

Wild River Technology (WRT), the leading supplier of signal integrity measurement and optimization test fixtures for high-speed channels at data rates of up to 224G, has announced the availability of a new advanced channel modeling solution that helps achieve extreme signal integrity design to 70GHz. Read the press release. The CMP-70 program continues the industry-first simulation-to-measurement collaboration with Cadence that was initially established with the CMP-50. Significant resources were dedicated to the development of the CMP-70 by Cadence and WRT over almost three years. The CMP-70 will be on display at DesignCon 2025 , January 28-30, in Cadence booth 827 to benchmark the Cadence Clarity 3D Solver . “I am not a fan of hype-based programs that simply get attention,” remarked Alfred P. Neves, WRT’s co-founder and chief technical officer. “Both Cadence and Wild River brought substantial skills to the table in this project as we continued our industry-first simulation-to-measurement collaboration. The result is a proven, robust and accurate platform that brings extreme signal integrity to 70GHz designs. This application package has also been instrumental in demonstrating the robust 3D EM simulation capability of the Cadence Clarity solver.” “We’re delighted to continue the joint development and validation program with WRT that started with the CMP-50,” said Gary Lytle, product management director at Cadence. “The skilled and experienced signal integrity technologists that both companies bring to the program results in a superior signal integrity solution for our mutual customers.” CMP-70 Solution Features The solution is available both in a standard configuration and as a custom solution for customer-specific stackups and fabrication. The primary target application is to support a 3D EM solver analysis modeling versus the time- and frequency-domain measurement methodologies. The solution features include: The CMP-70 platform, assembled and 100% TDR NIST traceable tested, with custom stands Material Identification overview web-based meeting including anisotropic 3D material identification A cross-section PCB report and structures for using as-fabricated geometries Measured S-parameters, pre-tested for quality (passivity/causality and resampled for time domain simulations) A host of novel crosstalk structures suited for 112G HD level project analysis PCB layout design files (NDA required) An EDA starter library including loss models with industry-first accurate surface roughness models Comprehensive training available for 3D EM analysis – correspondence, material ID in X-Y and Z axis for a host of EDA tools Industry-First Hausdorff Technique The WRT application package also includes an industry-first modified Hausdorff (MHD) technique , included as MATLAB code. This algorithmic approach provides an accurate way to compare two sets of measurements in multi-dimensional space to determine how well they match. The technique is used to compare the results simulated by the Clarity solver with those measured on the CMP-70 platform. The methodology and initial results are shown in the figure below, where the figure of merit (FOM) is calculated from 10, 35, and finally to 50GHz. The MHD algorithm requires a MATLAB license, but WRT also accommodates customer data as another option, where WRT provides the comparison between measured and simulated data. Additional Resources If you are attending DesignCon 2025 , be sure to stop by Cadence booth 827 to see WRT’s CMP-70 advanced channel modeling solution in action with the Clarity 3D Solver. Check out our on-demand webinar, " Validating Clarity 3D Solver Accuracy Through Measurement Correlation ." Learn more about the CMP-70 solution and the Clarity 3D Solver . For more information about Cadence’s full suite of integrated multiphysics simulation solutions, download our Multiphysics System Analysis Solutions Portfolio .

Hello,

I'm reading the Design specification for IP6514E.

We will use the DAC mode.

It would seem to be very simple but I don't see any code sequence, i.e.

  1.Write 03(Basic Read) to this register

  2, Write start adress to this register

  3. Write "execute" to this register

  4. Read the data from this register

Thanks,

Stefan

Read this blog to know more about the innovation behind Universal Connect Modules (UCM).(read more)

Hello,

I am aware of command script .svcf file that saves signals and loads them in while opening Simvision.

I am wondering, if there is a way for saving signals while we are in an interactive session and loading them next time when we open Simvision interactively.

Any ideas on how to do this?

Thank you in advance.

Swetha. C

Hi,

Current and valid TCAs for Functional Safety are readily available at the FuSa "one-stop shop".

But I have not been able to find any archive repository for access to the obsoleted versions.

I would need to have also v1.4 of Xcelum TCA to investigate exact changes wrt previous projects.

Anyone knows how to find it?

Best regards,  Lars

Hello All,

Following is the einstein's puzzle solved by cadence Incisive92  (solved in less than 3 seconds -> FAST!!!!!!)

Thanks,

Vinay Honnavara

Verification engineer at Keyu Tech

vinayh@keyutech.com

 // Author: Vinay Honnavara

// Einstein formulated this problem : he said that only 2% in the world can solve this problem
// There are 5 different parameters each with 5 different attributes
// The following is the problem

// -> In a street there are five houses, painted five different colors (RED, GREEN, BLUE, YELLOW, WHITE)

// -> In each house lives a person of different nationality (GERMAN, NORWEGIAN, SWEDEN, DANISH, BRITAIN)

// -> These five homeowners each drink a different kind of beverage (TEA, WATER, MILK, COFFEE, BEER),

// -> smoke different brand of cigar (DUNHILL, PRINCE, BLUE MASTER, BLENDS, PALL MALL)

// -> and keep a different pet (BIRD, CATS, DOGS, FISH, HORSES)


///////////////////////////////////////////////////////////////////////////////////////
// *************** Einstein's riddle is: Who owns the fish? ***************************
///////////////////////////////////////////////////////////////////////////////////////

/*
Necessary clues:

1. The British man lives in a red house.
2. The Swedish man keeps dogs as pets.
3. The Danish man drinks tea.
4. The Green house is next to, and on the left of the White house.
5. The owner of the Green house drinks coffee.
6. The person who smokes Pall Mall rears birds.
7. The owner of the Yellow house smokes Dunhill.
8. The man living in the center house drinks milk.
9. The Norwegian lives in the first house.
10. The man who smokes Blends lives next to the one who keeps cats.
11. The man who keeps horses lives next to the man who smokes Dunhill.
12. The man who smokes Blue Master drinks beer.
13. The German smokes Prince.
14. The Norwegian lives next to the blue house.
15. The Blends smoker lives next to the one who drinks water.
*/




typedef enum bit [2:0]  {red, green, blue, yellow, white} house_color_type;
typedef enum bit [2:0]  {german, norwegian, brit, dane, swede} nationality_type;
typedef enum bit [2:0]  {coffee, milk, water, beer, tea} beverage_type;
typedef enum bit [2:0]  {dunhill, prince, blue_master, blends, pall_mall} cigar_type;
typedef enum bit [2:0]  {birds, cats, fish, dogs, horses} pet_type;




class Einstein_problem;

    rand house_color_type house_color[5];
    rand nationality_type nationality[5];
    rand beverage_type beverage[5];
    rand cigar_type cigar[5];
    rand pet_type pet[5];
        rand int arr[5];
    
    constraint einstein_riddle_solver {
    
        
    
        foreach (house_color[i])
            foreach (house_color[j])
               if (i != j)
                house_color[i] != house_color[j];
        foreach (nationality[i])
            foreach (nationality[j])
               if (i != j)
                nationality[i] != nationality[j];
        foreach (beverage[i])
            foreach (beverage[j])
               if (i != j)
                beverage[i] != beverage[j];
        foreach (cigar[i])
            foreach (cigar[j])
               if (i != j)
                cigar[i] != cigar[j];
        foreach (pet[i])
            foreach (pet[j])
               if (i != j)
                pet[i] != pet[j];
    
    
        //1) The British man lives in a red house.
        foreach(nationality[i])
                (nationality[i] == brit) -> (house_color[i] == red);
                
        
        //2) The Swedish man keeps dogs as pets.
        foreach(nationality[i])
                (nationality[i] == swede) -> (pet[i] == dogs);
                
                
        //3) The Danish man drinks tea.        
        foreach(nationality[i])
                (nationality[i] == dane) -> (beverage[i] == tea);
        
        
        //4) The Green house is next to, and on the left of the White house.
        foreach(house_color[i])        
                 if (i<4)
                    (house_color[i] == green) -> (house_color[i+1] == white);
        
        
        //5) The owner of the Green house drinks coffee.
        foreach(house_color[i])
                (house_color[i] == green) -> (beverage[i] == coffee);
                
        
        //6) The person who smokes Pall Mall rears birds.
        foreach(cigar[i])
                (cigar[i] == pall_mall) -> (pet[i] == birds);
        
        
        //7) The owner of the Yellow house smokes Dunhill.
        foreach(house_color[i])
                (house_color[i] == yellow) -> (cigar[i] == dunhill);
        
        
        //8) The man living in the center house drinks milk.
        foreach(house_color[i])
                if (i==2) // i==2 implies the center house (0,1,2,3,4) 2 is the center
                    beverage[i] == milk;
        
        
        
        //9) The Norwegian lives in the first house.
        foreach(nationality[i])        
                if (i==0) // i==0 is the first house
                    nationality[i] == norwegian;
        
        
        
        //10) The man who smokes Blends lives next to the one who keeps cats.
        foreach(cigar[i])        
                if (i==0) // if the man who smokes blends lives in the first house then the person with cats will be in the second
                    (cigar[i] == blends) -> (pet[i+1] == cats);
        
        foreach(cigar[i])        
                if (i>0 && i<4) // if the man is not at the ends he can be on either side
                    (cigar[i] == blends) -> (pet[i-1] == cats) || (pet[i+1] == cats);
        
        foreach(cigar[i])        
                if (i==4) // if the man is at the last
                    (cigar[i] == blends) -> (pet[i-1] == cats);
        
        foreach(cigar[i])        
                if (i==4)
                    (pet[i] == cats) -> (cigar[i-1] == blends);
        
        
        //11) The man who keeps horses lives next to the man who smokes Dunhill.
        foreach(pet[i])
                if (i==0) // similar to the last case
                    (pet[i] == horses) -> (cigar[i+1] == dunhill);
        
        foreach(pet[i])        
                if (i>0 & i<4)
                    (pet[i] == horses) -> (cigar[i-1] == dunhill) || (cigar[i+1] == dunhill);
                    
        foreach(pet[i])        
                if (i==4)
                    (pet[i] == horses) -> (cigar[i-1] == dunhill);
                    


        //12) The man who smokes Blue Master drinks beer.
        foreach(cigar[i])
                (cigar[i] == blue_master) -> (beverage[i] == beer);
        
        
        //13) The German smokes Prince.
        foreach(nationality[i])        
                (nationality[i] == german) -> (cigar[i] == prince);
        

        //14) The Norwegian lives next to the blue house.
        foreach(nationality[i])
                if (i==0)
                    (nationality[i] == norwegian) -> (house_color[i+1] == blue);
        
        foreach(nationality[i])        
                if (i>0 & i<4)
                    (nationality[i] == norwegian) -> (house_color[i-1] == blue) || (house_color[i+1] == blue);
        
        foreach(nationality[i])        
                if (i==4)
                    (nationality[i] == norwegian) -> (house_color[i-1] == blue);
        

        //15) The Blends smoker lives next to the one who drinks water.            
        foreach(cigar[i])            
                if (i==0)
                    (cigar[i] == blends) -> (beverage[i+1] == water);
        
        foreach(cigar[i])        
                if (i>0 & i<4)
                    (cigar[i] == blends) -> (beverage[i-1] == water) || (beverage[i+1] == water);
                    
        foreach(cigar[i])        
                if (i==4)
                    (cigar[i] == blends) -> (beverage[i-1] == water);
        
    } // end of the constraint block
    


    // display all the attributes
    task display ;
        foreach (house_color[i])
            begin
                $display("HOUSE : %s",house_color[i].name());
            end
        foreach (nationality[i])
            begin
                $display("NATIONALITY : %s",nationality[i].name());
            end
        foreach (beverage[i])
            begin
                $display("BEVERAGE : %s",beverage[i].name());
            end
        foreach (cigar[i])
            begin
                $display("CIGAR: %s",cigar[i].name());
            end
        foreach (pet[i])
            begin
                $display("PET : %s",pet[i].name());
            end
        foreach (pet[i])
            if (pet[i] == fish)
                $display("THE ANSWER TO THE RIDDLE : The %s has %s ", nationality[i].name(), pet[i].name());
    
    endtask // end display
    
    
endclass




program main ;

    initial
        begin
            Einstein_problem ep;
            ep = new();
            if(!ep.randomize())
                $display("ERROR");
            ep.display();
        end
endprogram // end of main

        

Using the EMX solver, X-FAB design engineers can efficiently develop next-generation RF technology for the latest communication standards (including sub-6GHz 5G, mmWave, UWB, etc.), which are enabling technologies for communications and electric vehicle (EV) wireless applications. (read more)

Hi,

In our design team, we're looking for a strategy to make all cell views self-contained. We are struggling to do so when nport devices are involved.

The nport file requires a full path, whereas what we need is a relative path to the current path of the cell in which we're using the nport.

I have browsed through the forums & cadence support pages, but could not find a solution.

1) There is a proposal from Andrew to add the file directory in ADE option "Simulation Files." :https://community.cadence.com/cadence_technology_forums/f/rf-design/27167/s-parameter-datafile-path-in-nport . This, however, is not suitable, because the cell is not self contained.

2) The new cadence version off DataSource "cellView" in nport options:

This however is not suitable for us due to two reasons:

i- Somehow we don't get this option in the nport cell (perhaps some custom modification from our PDK team)

ii- Even if we had this option, it requires to select the library, which again makes it unsuitable: We often copy design libraries for derivative products using "Hierarchical Copy" feature. And when the library is copied, the nport will still be pointing to the old library. Thus, it is still not self-contained.

In principle, it should not be difficult (technically) to point to a text file relative to the cell directory (f.ex we can make a folder under the same cell with name "sparFiles" & place all spar files under this folder), however it does not seem to be possible.

Could you perhaps recommend us a work-around to achieve our goal: making the cells which contain nport devices self-contained so that when we copy a cell, we do not have to update all the nport file destinations ?

Thanks in advance.

My Cadence Version: IC23.1-64b.ISR4.51

My Spectre version: 23.1.0.362.isr5

Read through this blog to know more about the new Reliability Report view in Virtuoso ADE Assembler and Virtuoso ADE Explorer.(read more)

This blog introduces you to an efficient way to debug interface elements or connect modules in a mixed-signal simulation.(read more)

The Cadence Cerebrus Intelligent Chip Explorer is a revolutionary, machine learning-driven, automated approach to chip design flow optimization. Block engineers specify the design goals, and Cadence Cerebrus will intelligently optimize the Cadence digital full flow to meet the power, performance, and area (PPA) goals in a completely automated way. Use Cerebrus Apps to optimize some aspects of the design as well.

Running a full RTL to GDSII flow, Cadence Cerebrus has a lot of possibilities and combinations of different tool settings to explore.

Using the knowledge from previous runs, combined with on-the-fly analysis within the flow, Cadence Cerebrus can assess many settings combinations and fine-tune the flow accordingly in a very efficient manner.

As technology advances, projects become bigger and way more complex than before. The ability of a single engineer to run simultaneously a large number of blocks in a traditional way is limited. Cadence Cerebrus allows a single engineer to work more efficiently and implement more blocks, while maintaining the same or even better PPA, using compute power.

Being such a revolutionary tool, integrating Cerebrus into your existing flow is surprisingly simple as it can wrap around any existing flow scripts.

Please join me in this course, to learn about the features and basics of Cadence Cerebrus Intelligent Chip Explorer.

We’ll walk through the tool setting stage, explain what is a primitive and how it effects our run, talk about the cost function and the run goals.

We’ll understand the concept of scenarios, learn how to analyze the results of the different runs, and compare them.

In addition, we’ll talk about basic debug rules and methods to analyze failures.

Sounds Interesting?

Please join our “live” one-day Cadence Cerebrus Intelligent Chip Explorer Training @Cadence Feldkirchen planned for October 9th, 2024!

For more details and registration, please contact Training Germany.

If you would like to have an instructor-led training session in another region please contact your local training department.

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