People with cannabis use disorder (CUD) report more sleep problems than those without, and their disrupted sleep may affect visual learning memory.

A study published in The American Journal of Drug and Alcohol found that long-term cannabis users experience interfered with sleep and memory. The findings

A study published in The American Journal of Drug and Alcohol found that long-term cannabis users experience interfered with sleep and memory. The findings

Alzheimer's patients with high levels of tau and amyloid-beta experience faster cognitive decline.

Sophia Antipolis, 02 January 2024
*This article was updated with additional details on January 4, 2024.

In Memory of Thomas Haug 

All at ETSI are saddened to hear of the passing on December 9, 2023 of one of the great early champions of European mobile communications. Thomas Haug made a massive contribution to the work that led to the creation of ETSI in 1988, as one of the leaders in the Nordic project for cellular communication (NMT) and Chair of the CEPT* GSM group.

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For decades, Rosanne Cash has soared through the ranks of music with her powerhouse poetic skills and wistful reflections on her past. This hour, we explore Rosanne's life and legacy through her music.

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Our brains are magnificently complex - and highly fallible. This hour, neuroscientist and novelist Lisa Genova explains how to keep our brains healthy and what to do when something goes wrong.

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Original broadcast date: July 29, 2022. For decades, Rosanne Cash has soared through the ranks of music with her powerhouse poetic skills and wistful reflections on her past. This hour, we explore Rosanne's life and legacy through her music.

TED Radio Hour+ subscribers now get access to bonus episodes, with more ideas from TED speakers and a behind the scenes look with our producers. A Plus subscription also lets you listen to regular episodes (like this one!) without sponsors. Sign-up at: plus.npr.org/ted

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Original broadcast date: December 23, 2022. Our brains are magnificently complex - and highly fallible. This hour, neuroscientist and novelist Lisa Genova explains how to keep our brains healthy and what to do when something goes wrong.

TED Radio Hour+ subscribers now get access to bonus episodes, with more ideas from TED speakers and a behind the scenes look with our producers. A Plus subscription also lets you listen to regular episodes (like this one!) without sponsors. Sign-up at plus.npr.org/ted.

Learn more about sponsor message choices: podcastchoices.com/adchoices

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Toronto, ON – University of Toronto and MIT researchers have discovered important differences between lower and higher-income children in their ability to use “working memory,” a key brain function responsible for everything from remembering a phone number to doing math in your head. Using functional MRI (fMRI) to measure and map the brain activity of […]

This episode features a discussion with Dan Grossman about an essay paper he wrote for this year's OOPSLA conference. The paper is about an analogy between garbage collection and transactional memory. In addition to seeing the beauty of the analogy, the discussion also serves as a good introduction to transactional memory (which was mentioned in the Goetz/Holmes episode) and - to some extent - to garbage collection.

In this Episode we're discussing patterns for small memory software with the authors of the like-named book Charles Weir and James Noble. We look at various aspects of the small memory problem: How can you manage memory use across a whole system? What can you do when you have run out of primary storage? How can you fit a quart of data into a pint pot of memory? How can you reduce the memory needed for your data? How do you allocate memory to store your data structures? Answers to all those questions are provided in this Episode, and of course in their book.

In this episode, Robert talks with Nati Shalom about the emergence of large-system architectures consisting of a grid of high-memory nodes.

Howard Chu, CTO of Symas Corp and chief architect of the OpenLDAP Project, discusses the key technical features of the Lightning Memory-mapped Database (LMDB) that make it one of the fastest, most efficient and safest embedded data stores in the world.

“Justice for Tyre will always be the case. His legacy is more important than anything,'' said the father of the teen who died on the Orlando Free Fall drop ride.

Apple has increased the base amount of memory in the M2 and M3 MacBook Air models, possibly to ensure optimal performance for Apple Intelligence.

The massive networks of neurons in our brains produce complex behaviors, like actions and thought. Now physicists want to understand the laws that govern this emergent phenomena.

Our memories are more reliable than experts predicted

Library - Art Library, Location - OSIZ, Call number - FOLIO TR183 .M38 2016

Library - Art Library, Location - LIB, Call number - Video record 43522 DVD

Location: Electronic Resource- 

Destine Literare, August 2013: In Memory of Ninos Aho

The 'Leftovers' actor struggled to remember his name and was mistakenly told that he needed a brain surgery following a serious accident in New York City.

I am very sad to let the MeFi community know that one of its staunchest supporters and all around good guy digaman (Steve Silberman) has passed away as reported by his spouse on Bluesky. For people who didn't know digaman well, you might know his book NeuroTribes (MeFi post), the Grateful Dead box set he co-produced So Many Roads, or one of his 184 posts to MetaFilter (all from 2010 and before). He was always a great internet friend to me especially lately over on Mastodon and I know I will miss his voice. May his memory be a blessing, we are lucky to have known him.

As public support for marijuana decriminalization grows, new research is shedding light on the drug’s impact on sleep and memory.

When Nas was introduced to hip hop.

He was traded to L.A. last month.

BUEI Films is set the screen the 2023 Drama, ‘Memory’, directed by Michel Franco, starring Jessica Chastain and Peter Sarsgaard, on Saturday, April 27. A spokesperson said, “Films at BUEI continue at the Bermuda Underwater Exploration Institute [BUEI] with the screening of the 2023 Drama, ‘Memory’ on Saturday, April 27th at 7:30pm in our Tradewinds […]

When Windows tells you it's out of memory, what does it mean and what can you do about it?

How Do I Tell Which Program Is Using So Much Memory? from Ask Leo!.
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Cybersecurity researchers have flagged a new ransomware family called Ymir that was deployed in an attack two days after systems were compromised by a stealer malware called RustyStealer. "Ymir ransomware introduces a unique combination of technical features and tactics that enhance its effectiveness," Russian cybersecurity vendor Kaspersky said. "Threat actors leveraged an unconventional blend

The post Party Hard In Memory of the Chernobyl Victims appeared first on English Russia.

Identification of antibody-binding epitopes is crucial to understand immunological mechanisms. It is of particular interest for allergenic proteins with high cross-reactivity as observed in the lipid transfer protein (LTP) syndrome, which is characterized by severe allergic reactions. Art v 3, a pollen LTP from mugwort, is frequently involved in this cross-reactivity, but no antibody-binding epitopes have been determined so far. To reveal human IgE-binding regions of Art v 3, we produced three murine high-affinity mAbs, which showed 70–90% coverage of the allergenic epitopes from mugwort pollen–allergic patients. As reliable methods to determine structural epitopes with tightly interacting intact antibodies under native conditions are lacking, we developed a straightforward NMR approach termed hydrogen/deuterium exchange memory (HDXMEM). It relies on the slow exchange between the invisible antigen-mAb complex and the free 15N-labeled antigen whose 1H-15N correlations are detected. Due to a memory effect, changes of NH protection during antibody binding are measured. Differences in H/D exchange rates and analyses of mAb reactivity to homologous LTPs revealed three structural epitopes: two partially cross-reactive regions around α-helices 2 and 4 as well as a novel Art v 3–specific epitope at the C terminus. Protein variants with exchanged epitope residues confirmed the antibody-binding sites and revealed strongly reduced IgE reactivity. Using the novel HDXMEM for NMR epitope mapping allowed identification of the first structural epitopes of an allergenic pollen LTP. This knowledge enables improved cross-reactivity prediction for patients suffering from LTP allergy and facilitates design of therapeutics.

The recent structural elucidation of ex vivo Drosophila Orb2 fibrils revealed a novel amyloid formed by interdigitated Gln and His residue side chains belonging to the prion-like domain. However, atomic-level details on the conformational transitions associated with memory consolidation remain unknown. Here, we have characterized the nascent conformation and dynamics of the prion-like domain (PLD) of Orb2A using a nonconventional liquid-state NMR spectroscopy strategy based on 13C detection to afford an essentially complete set of 13Cα, 13Cβ, 1Hα, and backbone 13CO and 15N assignments. At pH 4, where His residues are protonated, the PLD is disordered and flexible, except for a partially populated α-helix spanning residues 55–60, and binds RNA oligos, but not divalent cations. At pH 7, in contrast, His residues are predominantly neutral, and the Q/H segments adopt minor populations of helical structure, show decreased mobility and start to self-associate. At pH 7, the His residues do not bind RNA or Ca2+, but do bind Zn2+, which promotes further association. These findings represent a remarkable case of structural plasticity, based on which an updated model for Orb2A functional amyloidogenesis is suggested.

For data that is being loaded from a SAS Stored Process Server, an insertion process might fail to a MySQL database with a warning, as well as an error message that says "During insert: Incorrect datetime value…"

Memory politics: the challenge of commemoration in post-Soviet Eastern Europe and the Caucasus 5 October 2021 — 1:00PM TO 2:30PM Anonymous (not verified) 21 September 2021 Online

This event explores how to address memory and commemoration in the former Soviet states, considering their role in political processes and violent conflict. 

How the past is remembered and commemorated plays a large role – perhaps too large – in contemporary political debates and in how conflicts are negotiated.

Perceptions of history influence people’s actions and are used to judge or dismiss the actions of others. Nowhere is this more so than in the political, territorial and social debates and disputes across the former Soviet Union.
 
This event examines how to address the problems caused by entrenched memory debates – and proposes a framework for ‘ethical political commemoration’ for use across historical enquiry, political processes, and conflict transformation initiatives.

The speakers explore the topic through the context of Turkey and the Armenian genocide, as well as more broadly through their own experiences in conflict transformation and peace processes.

A former Jeopardy! winner led a new study that probes how linked memory systems may give trivia buffs an edge in their game

Higher consumption of these foods was linked to improved memory by the study.

Marloes J. A. G. Henckens
Aug 12, 2009; 29:10111-10119
BehavioralSystemsCognitive

Robbert Havekes
Dec 12, 2012; 32:18137-18149
BehavioralSystemsCognitive

Randy L. Buckner
Aug 24, 2005; 25:7709-7717
Neurobiology of Disease

Sleep is known to drive the consolidation of motor memories. During nonrapid eye movement (NREM) sleep, the close temporal proximity between slow oscillations (SOs) and spindles ("nesting" of SO-spindles) is known to be essential for consolidation, likely because it is closely associated with the reactivation of awake task activity. Interestingly, recent work has found that spindles can occur in temporal clusters or "trains." However, it remains unclear how spindle trains are related to the nesting phenomenon. Here, we hypothesized that spindle trains are more likely when SOs co-occur in the prefrontal and motor cortex. We conducted simultaneous neural recordings in the medial prefrontal cortex (mPFC) and primary motor cortex (M1) of male rats training on the reach-to-grasp motor task. We found that intracortically recorded M1 spindles are organized into distinct temporal clusters. Notably, the occurrence of temporally precise SOs between mPFC and M1 was a strong predictor of spindle trains. Moreover, reactivation of awake task patterns is much more persistent during spindle trains in comparison with that during isolated spindles. Together, our work suggests that the precise coupling of SOs across mPFC and M1 may be a potential driver of spindle trains and persistent reactivation of motor memory during NREM sleep.

It is well established that holding information in working memory (WM) elicits sustained stimulus-specific patterns of neural activity. Nevertheless, here we provide evidence for a distinct class of neural activity that tracks the number of individuated items in working memory, independent of the type of visual features stored. We present two EEG studies of young adults of both sexes that provide robust evidence for a signal tracking the number of individuated representations in working memory, regardless of the specific feature values stored. In Study 1, subjects maintained either colors or orientations across separate blocks in a single session. We found near-perfect generalization of the load signal between these two conditions, despite being able to simultaneously decode which feature had been voluntarily stored. In Study 2, participants attended to two features with very distinct cortical representations: color and motion coherence. We again found evidence for a neural load signal that robustly generalized across these distinct visual features, even though cortically disparate regions process color and motion coherence. Moreover, representational similarity analysis provided converging evidence for a content-independent load signal, while simultaneously showing that unique variance in EEG activity tracked the specific features that were stored. We posit that this load signal reflects a content-independent "pointer" operation that binds objects to the current context while parallel but distinct neural signals represent the features that are stored for each item in memory.

Scientists have created "a form of information immortality" meant to instruct future species on how to recreate humans. But who, or what, will find it?

New lobby exhibit celebrates “things that aren’t there anymore” Do you remember rocking at the Stone Balloon; enjoying a muskrat meal at The Wagon Wheel; or having a shopping spree at Wanamaker’s?  If you don’t the Delaware Public Archives does. Starting in April 2024, the DPA will kick off a celebration and remembrance of things […]

President Biden ordered flags at all U.S. government buildings and facilities to be flown at half-staff until sunset on February 26, 2021 in memory of the more than 500,000 Americans who have died from COVID-19. In concurrence with the President’s order, Governor Carney has ordered both the U.S. and Delaware flags at state buildings and […]

Read the in depth Review of CORSAIR DOMINATOR PLATINUM RGB First Edition DDR5 DRAM Memory Kit PC Components. Know detailed info about CORSAIR DOMINATOR PLATINUM RGB First Edition DDR5 DRAM Memory Kit configuration, design and performance quality along with pros & cons, Digit rating, verdict based on user opinions/feedback.

The generative AI market is experiencing rapid growth, driven by the increasing parameter size of Large Language Models (LLMs). This growth is pushing the boundaries of performance requirements for training hardware within data centers. For an in-depth look at this, consider the insights provided in "HBM3E: All About Bandwidth". Once trained, these models are deployed across a diverse range of applications. They are transforming sectors such as finance, meteorology, image and voice recognition, healthcare, augmented reality, high-speed trading, and industrial, to name just a few.

The critical process that utilizes these trained models is called AI inference. Inference is the capability of processing real-time data through a trained model to swiftly and effectively generate predictions that yield actionable outcomes. While the AI market has primarily focused on the requirements of training infrastructure, there is an anticipated shift towards prioritizing inference as these models are deployed.

The computational power and memory bandwidth required for inference are significantly lower than those needed for training. Inference engines typically need between 300-700GB/s of memory bandwidth, compared to 1-3TB/s for training. Additionally, the cost of inference needs to be lower, as these systems will be widely deployed not only in data centers but also at the network's edge (e.g., 5G) and in end-user equipment like security cameras, cell phones, and automobiles.

When designing an AI inference engine, there are several memory options to consider, including DDR, LPDDR, GDDR, and HBM. The choice depends on the specific application, bandwidth, and cost requirements. DDR and LPDDR offer good memory density, HBM provides the highest bandwidth but requires 2.5D packaging, and GDDR offers high bandwidth using standard packaging and PCB technology.

The GDDR7 standard, announced by JEDEC in March of this year, features a data rate of up to 192GB/s per device, a chip density of 32Gb, and the latest data integrity features. The high data rate is achieved by using PAM3 (Pulse Amplitude Modulation) with 3 levels (+1, 0, -1) to transmit 3 bits over 2 cycles, whereas the current GDDR6 generation uses NRZ (non-return-to-zero) to transmit 2 bits over 2 cycles.

GDDR7 offers many advantages for AI Inference having the best balance of bandwidth and cost. For example, an AI Inference system requiring 500GB/s memory bandwidth will need only 4 GDDR7 DRAM running at 32Gbp/s (32 data bits x 32Gbp/s per pin = 1024Gb/s per DRAM). The same system would use 13 LPDDR5X PHYs running at 9.6Gbp/s, which is currently the highest data rate available (32 data bits x 9.6Gb/s = 307Gb/s per DRAM).

Cadence stands at the forefront of AI inference hardware support, being the first IP company to roll out GDDR7 PHYs capable of impressive speeds up to 36Gb/s across various process nodes. This milestone builds on Cadence's established leadership in GDDR6 PHY IP, which has been available since 2019. The company caters to a diverse client base spanning AI inference, graphics, automotive, and networking equipment.

While GDDR7 continues to utilize standard PCB board technology, the increased signal speeds seen in GDDR6 (20Gbp/s) and now GDDR7 (36Gb/s) calls for careful attention with the physical design to ensure optimized system performance. In addition to providing the PHY, Cadence also offers comprehensive PCB and package reference design, which are essential in helping customers achieve optimal signal and power integrity (SI/PI) for their systems.

Cadence is dedicated to ensuring customer success beyond just providing hardware. They provide expert support in SI/PI, collaborating closely with customers throughout the design process. This approach ensures that customers can benefit from Cadence's expertise in navigating the complexities of high-speed design and achieving optimal performance in their AI inference systems.

As the AI market continues to advance, Cadence remains at the forefront by offering a comprehensive memory IP portfolio tailored for every segment of this dynamic market. From DDR5 and HBM3E, which cater to the intensive demands of training in servers and high-performance computing (HPC), to LPDDR5X designed for low-end inference at the network edge and in consumer devices, Cadence's offerings cover a wide range of applications.

Looking to the future, Cadence is dedicated to innovating at the forefront of memory system performance, ensuring that the evolving needs of AI training and inference are met with the highest standards of excellence. Whether it's pushing the boundaries with GDDR7 or exploring new technologies, Cadence is dedicated to driving the AI revolution forward, one breakthrough at a time.

Learn more about Cadence GDDR7 PHY

Learn more about Cadence Simulation VIP for GDDR7.

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.

DDR5 DIMM Architectures The DDR5 generation of Double Data Rate DRAM memories has experienced rapid adoption in recent years. In particular, the JEDEC-defined DDR5 Dual Inline Memory Module (DIMM) cards have become a mainstay for systems looking for high-density, high-bandwidth, off-chip random access memory[1]. Within a short time, the DIMM architecture evolved from an interconnected hierarchy of only SDRAM memory devices (UDIMM[2]) to complex subsystems of interconnected components (RDIMM/LRDIMM/MRDIMM[3]). DIMM Designs and Popular Verification Use Cases The growing complexity of the DIMMs presented a challenge for pre-silicon verification engineers who could no longer simply validate against single DDR5 SDRAM memory models. They needed to consider how their designs would perform against DIMMs connected to each channel and operating at gigahertz clock speeds. To address this verification gap, Cadence developed DDR5 DIMM Memory Models that encapsulated all of the architectural complexities presented by real-world DIMMs based on a robust, easy-to-use, easy-to-debug, and easy-to-reconfigure methodology. This memory-subsystem-in-a-single-instance model has seen explosive adoption among the traditional IP Developer and SOC Integrator customers of Cadence Memory Models. The Cadence DIMM models act as a single unit with all of the relevant DIMM components instantiated and interconnected within, and with all AC/Timing parameters among the various components fully matched out-of-the-box, based on JEDEC specifications as well as datasheets of actual devices in the market. The typical use-case for the DIMM models has been where the DUT is a DDR5 Memory Controller + PHY IP stack, and the validation plan mandated compliance with the JEDEC standards and Memory Device vendor datasheets. Unique Use Case for the DIMM Discrete Component Models Although the Cadence DIMM models have enjoyed tremendous proliferation because of their cohesive implementation and unified user API, the actual DIMM Models are built on top of powerful, flexible discrete component models, each of which was designed to stand on its own as a complete SystemVerilog UVM-based VIP. All of these discrete component models exist in the Cadence VIP Catalog as standalone VIPs, complete with their own protocol compliance checking capabilities and their own configuration mappings comprehensively modeling individual AC/Timing parameters. Because of this deliberate design decision, the Cadence DIMM Discrete Component Models can support a unique use-case scenario. Some users seek to develop IC Designs for the various DIMM components. Such users need verification environments that can model the individual components of a DIMM and allow them the option to replace one or another component with their Component Design IP. They can then validate that their component design is fully compatible with the rest of the components on the DIMM and meets the integrity of the overall DIMM compliance with JEDEC standards or Memory Vendor datasheets. The Cadence Memory VIP portfolio today includes various examples that demonstrate how customers can create DIMM “wrappers” by selecting from among the available DIMM discrete component models and “stitching” them together to build their own custom testbench around their specific Component Design IP. A Solution for Unique Component Scenarios The Cadence DDR5 DIMM Memory Models and DIMM Discrete Component Models can provide users with a flexible approach to validating their specific component designs with a fully populated pre-silicon environment. Augmented Verification Capabilities When the DIMM “wrapper” model is augmented with the Cadence DFI VIP[4] that can simulate an MC+PHY stack and offers a SystemVerilog UVM test API to the verification engineer, the overall testbench transforms into a formidable pre-silicon validation vehicle. The DFI VIP is designed as a combination of an independent DFI MC VIP and a DFI PHY VIP connected to each other via the DFI Standard Interface and capable of operating seamlessly as a single unit. It presents a UVM Sequence API to the user into the DFI MC VIP with the Memory Interface of the PHY VIP connected to the DIMM “wrapper” model. With this testbench in hand, the user can then fully take advantage of the UVM Sequence Library that comes with the DFI VIP to enable deep validation of their Component Design inside the DIMM “wrapper” model. Verification Capabilities Further Enhanced A possible further enhancement comes with the potential addition of an instance of the Cadence DIMM Memory Model in a Passive Monitor mode at the DRAM Memory Interface. The DIMM Passive Monitor consumes the same configuration describing the DIMM “wrapper” in the testbench, and thus can act as a reference model for the DIMM wrapper. If the DIMM Passive Monitor responds successfully to accesses from the DFI VIP, but the DIMM wrapper does not, then it exposes potential bugs in the DUT Components or in the settings of their AC/Timing parameters inside the DIMM wrapper. Debuggability, Interface Visibility, and Protocol Compliance One of the key benefits of the DIMM Discrete Component Models that become manifest, whether in terms of the unique use-case scenario described here, or when working with the wholly unified DDR5 DIMM Memory Models, is the increased debuggability of the protocol functionality. The intentional separation of the discrete components of a DIMM allows the user to have full visibility of the memory traffic at every datapath landmark within a DIMM structure. For example, in modeling an LRDIMM or MRDIMM, the interface between the RCD component and the SDRAM components, the interface between the RCD component and the DB components, and the interface between the SDRAM components and the DB components—all are visible and accessible to the user. The user has full access to dump the values and states of the wire interconnects at these interfaces to the waveform viewer and thus can observe and correlate the activity against any protocol violations flagged in the trace logs by any one or more of the DIMM Discrete Component Models. Access to these interfaces is freely available when using the DIMM Discrete Component Models. On the unified DDR5 DIMM Memory Models, a feature called Debug Ports enables the same level of visibility into the individual interconnects amidst the SDRAM components, RCD components, and DB components. When combined with the Waveform Debugger[5] capability that comes built-in with the VIPs and Memory Models offered by Cadence and used with the Cadence Verisium Debug[6] tool, the enhanced debuggability becomes a powerful platform. With these debug accesses enabled, the user can pull out transaction streams, chip state and bank state streams, mode register streams, and error message streams all right next to their RTL signals in the same Verisium Debug waveform viewer window to debug failures all in one place. The Verisium Debug tool also parses all of the log files to probe and extract messages into a fully integrated Smart Log in a tabbed window fully hyperlinked to the waveform viewer, all at your fingertips. A Solution for Every Scenario Cadence's DDR5 DIMM Memory Models and DIMM Discrete Component Models , partnered with the Cadence DFI VIP, can provide users with a robust and flexible approach to validating their designs thoroughly and effectively in pre-silicon verification environments ahead of tapeout commitments. The solution offers unparalleled latitude in debuggability when the Debug Ports and Waveform Debugger functions of the Memory Models are switched on and boosted with the use of the Cadence Verisium Debug tool. [1] Shyam Sharma, DDR5 DIMM Design and Verification Considerations , 13 Jan 2023. [2] Shyam Sharma, DDR5 UDIMM Evolution to Clock Buffered DIMMs (CUDIMM) , 23 Sep 2024. [3] Kos Gitchev, DDR5 12.8Gbps MRDIMM IP: Powering the Future of AI, HPC, and Data Centers , 26 Aug 2024. [4] Chetan Shingala and Salehabibi Shaikh, How to Verify JEDEC DRAM Memory Controller, PHY, or Memory Device? , 29 Mar 2022. [5] Rahul Jha, Cadence Memory Models - The Gold Standard , 15 Apr 2024. [6] Manisha Pradhan, Accelerate Design Debugging Using Verisium Debug , 11 Jul 2023.

When the Arduino Mega2560 is added to the first serial port, the dynamic memory is 2000 bytes, and when the second serial serial is added, the dynamic memory is 4000 bytes. Now I need to add the third Serial serial port. The dynamic memory is 6000 bytes. Due to the many variables in the program itself, the dynamic memory is not enough. Please help me how to save the dynamic memory?