Pub Date : 2025-01-28DOI: 10.1109/LCA.2025.3528276
Sudhanva Gurumurthi;Mattan Erez
{"title":"Editorial: A Letter From the Editor-in-Chief of IEEE Computer Architecture Letters","authors":"Sudhanva Gurumurthi;Mattan Erez","doi":"10.1109/LCA.2025.3528276","DOIUrl":"https://doi.org/10.1109/LCA.2025.3528276","url":null,"abstract":"","PeriodicalId":51248,"journal":{"name":"IEEE Computer Architecture Letters","volume":"24 1","pages":"iii-iv"},"PeriodicalIF":1.4,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10856691","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-28DOI: 10.1109/LCA.2025.3534831
Qirong Xia;Houxiang Ji;Yang Zhou;Nam Sung Kim
Data compression has been widely used by datacenters to decrease the consumption of not only the memory and storage capacity but also the interconnect bandwidth. Nonetheless, the CPU cycles consumed for data compression notably contribute to the overall datacenter taxes. To provide a cost-efficient data compression capability for datacenters, Intel has introduced QuickAssist Technology (QAT), a PCIe-attached data-compression accelerator. In this work, we first comprehensively evaluate the compression/decompression performance of the latest on-chip QAT accelerator and then compare it with that of the previous-generation off-chip QAT accelerator. Subsequently, as a compelling application for QAT, we take a Linux memory optimization kernel feature: compressed cache for swap pages (zswap), re-implement it to use QAT efficiently, and then compare the performance of QAT-based zswap with that of CPU-based zswap. Our evaluation shows that the deployment of CPU-based zswap increases the tail latency of a co-running latency-sensitive application, Redis by 3.2-12.1×, while that of QAT-based zswap does not notably increase the tail latency compared to no deployment of zswap.
{"title":"Hardware-Accelerated Kernel-Space Memory Compression Using Intel QAT","authors":"Qirong Xia;Houxiang Ji;Yang Zhou;Nam Sung Kim","doi":"10.1109/LCA.2025.3534831","DOIUrl":"https://doi.org/10.1109/LCA.2025.3534831","url":null,"abstract":"Data compression has been widely used by datacenters to decrease the consumption of not only the memory and storage capacity but also the interconnect bandwidth. Nonetheless, the CPU cycles consumed for data compression notably contribute to the overall datacenter taxes. To provide a cost-efficient data compression capability for datacenters, Intel has introduced QuickAssist Technology (QAT), a PCIe-attached data-compression accelerator. In this work, we first comprehensively evaluate the compression/decompression performance of the latest <italic>on-chip</i> QAT accelerator and then compare it with that of the previous-generation <italic>off-chip</i> QAT accelerator. Subsequently, as a compelling application for QAT, we take a Linux memory optimization kernel feature: compressed cache for swap pages (<monospace>zswap</monospace>), re-implement it to use QAT efficiently, and then compare the performance of QAT-based <monospace>zswap</monospace> with that of CPU-based <monospace>zswap</monospace>. Our evaluation shows that the deployment of CPU-based <monospace>zswap</monospace> increases the tail latency of a co-running latency-sensitive application, Redis by 3.2-12.1×, while that of QAT-based <monospace>zswap</monospace> does not notably increase the tail latency compared to no deployment of <monospace>zswap</monospace>.","PeriodicalId":51248,"journal":{"name":"IEEE Computer Architecture Letters","volume":"24 1","pages":"57-60"},"PeriodicalIF":1.4,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10856688","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143619076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-27DOI: 10.1109/LCA.2025.3534188
Zhenlong Ma;Ning Kang;Fan Yang;Chongyang Hong;Jing Xu;Guojun Yuan;Peiheng Zhang;Zhan Wang;Ninghui Sun
RDMA network is being widely deployed in data centers, high-performance computing, and AI clusters. By offloading the network processing protocol stack to hardware, RDMA bypasses the operating system kernel, thereby enabling high performance and low CPU overhead. However, the protocol processing demands substantial communication resources, and due to the limited hardware resources, commercial NICs (Network Interface Cards) experience a significant number of cache misses in large-scale connection scenarios. This results in performance degradation, indicating that RDMA lacks scalability. In this paper, we first analyze the characteristics of resource access in RDMA. Based on these characteristics, we propose a resource access prediction and prefetching mechanism in the hardware, which preemptively fetches the resources required by the protocol processing pipeline to the on-chip cache. This mechanism increases the NIC’s cache hit ratio. Evaluation results demonstrate that our approach improves throughput by 125% and reduces latency by 17.9% under large-scale communication scenarios.
{"title":"Toward Scalable RDMA Through Resource Prefetching","authors":"Zhenlong Ma;Ning Kang;Fan Yang;Chongyang Hong;Jing Xu;Guojun Yuan;Peiheng Zhang;Zhan Wang;Ninghui Sun","doi":"10.1109/LCA.2025.3534188","DOIUrl":"https://doi.org/10.1109/LCA.2025.3534188","url":null,"abstract":"RDMA network is being widely deployed in data centers, high-performance computing, and AI clusters. By offloading the network processing protocol stack to hardware, RDMA bypasses the operating system kernel, thereby enabling high performance and low CPU overhead. However, the protocol processing demands substantial communication resources, and due to the limited hardware resources, commercial NICs (Network Interface Cards) experience a significant number of cache misses in large-scale connection scenarios. This results in performance degradation, indicating that RDMA lacks scalability. In this paper, we first analyze the characteristics of resource access in RDMA. Based on these characteristics, we propose a resource access prediction and prefetching mechanism in the hardware, which preemptively fetches the resources required by the protocol processing pipeline to the on-chip cache. This mechanism increases the NIC’s cache hit ratio. Evaluation results demonstrate that our approach improves throughput by 125% and reduces latency by 17.9% under large-scale communication scenarios.","PeriodicalId":51248,"journal":{"name":"IEEE Computer Architecture Letters","volume":"24 1","pages":"77-80"},"PeriodicalIF":1.4,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study investigates the performance of serving large language models (LLMs) with a focus on the high-bandwidth interconnect between GPU and CPU using a real NVIDIA Grace Hopper Superchip. This architecture features a GPU-centric memory tiering system, comprising a performance tier with GPU memory and a capacity tier with host memory. We revisit a conventional pipelined execution for LLM inference, utilizing host memory connected via NVLink alongside GPU memory. For the Llama-3.1 8B base (FP16) model, such a GPU-centric tiered memory system meets the target latency requirements for both prefill and decoding while improving throughput compared to the in-memory case, where all model weights are maintained in GPU memory. However, even with NVLink-connected CPU memory, meeting latency constraints for large models like the 70B and 405B FP16 models remains challenging. To address this, we explore the efficacy of model quantization (e.g., AWQ) along with the pipelined execution. Our evaluation reveals that the model quantization makes the pipelined execution a viable solution for serving large models. For the Llama-3.1 70B and 405B AWQ models, we show that the pipelined execution achieves 1.6× and 2.9× throughput improvement, respectively, compared to the in-memory only case, while meeting the latency constraint.
{"title":"GPU-Centric Memory Tiering for LLM Serving With NVIDIA Grace Hopper Superchip","authors":"Woohyung Choi;Jinwoo Jeong;Hanhwi Jang;Jeongseob Ahn","doi":"10.1109/LCA.2025.3533588","DOIUrl":"https://doi.org/10.1109/LCA.2025.3533588","url":null,"abstract":"This study investigates the performance of serving large language models (LLMs) with a focus on the high-bandwidth interconnect between GPU and CPU using a real NVIDIA Grace Hopper Superchip. This architecture features a GPU-centric memory tiering system, comprising a performance tier with GPU memory and a capacity tier with host memory. We revisit a conventional pipelined execution for LLM inference, utilizing host memory connected via NVLink alongside GPU memory. For the Llama-3.1 8B base (FP16) model, such a GPU-centric tiered memory system meets the target latency requirements for both prefill and decoding while improving throughput compared to the in-memory case, where all model weights are maintained in GPU memory. However, even with NVLink-connected CPU memory, meeting latency constraints for large models like the 70B and 405B FP16 models remains challenging. To address this, we explore the efficacy of model quantization (e.g., AWQ) along with the pipelined execution. Our evaluation reveals that the model quantization makes the pipelined execution a viable solution for serving large models. For the Llama-3.1 70B and 405B AWQ models, we show that the pipelined execution achieves 1.6× and 2.9× throughput improvement, respectively, compared to the in-memory only case, while meeting the latency constraint.","PeriodicalId":51248,"journal":{"name":"IEEE Computer Architecture Letters","volume":"24 1","pages":"33-36"},"PeriodicalIF":1.4,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Modern server-class CPUs are introducing special-purpose accelerators on the same chip to improve performance and efficiency for data-intensive applications. This paper presents a case for accelerating data migrations in operating systems with the Data Streaming Accelerator (DSA), a new feature by Intel. To the best of our knowledge, this is the first study that exploits a hardware-assisted data migration scheme in the operating system. We identify which Linux kernel components can benefit from the hardware acceleration, particularly focusing on the kernel subsystems that rely on the migrate_pages() kernel function. As the hardware accelerator is not suitable for transferring a small amount of data due to the HW setup overhead, this preliminary study concentrates on the design and implementation of accelerating migrate_pages() with DSA. We prototype a DSA-enabled Linux kernel and evaluate its effectiveness through two benchmarks demonstrating real-world page compaction (kcompactd) and promotion (kdamond) scenarios. In both cases, our prototype demonstrates improved throughput in page migration, benefiting both the kernel subsystem and applications.
{"title":"Accelerating Page Migrations in Operating Systems With Intel DSA","authors":"Jongho Baik;Jonghyeon Kim;Chang Hyun Park;Jeongseob Ahn","doi":"10.1109/LCA.2025.3530093","DOIUrl":"https://doi.org/10.1109/LCA.2025.3530093","url":null,"abstract":"Modern server-class CPUs are introducing special-purpose accelerators on the same chip to improve performance and efficiency for data-intensive applications. This paper presents a case for accelerating data migrations in operating systems with the Data Streaming Accelerator (DSA), a new feature by Intel. To the best of our knowledge, this is the first study that exploits a hardware-assisted data migration scheme in the operating system. We identify which Linux kernel components can benefit from the hardware acceleration, particularly focusing on the kernel subsystems that rely on the <monospace>migrate_pages()</monospace> kernel function. As the hardware accelerator is not suitable for transferring a small amount of data due to the HW setup overhead, this preliminary study concentrates on the design and implementation of accelerating <monospace>migrate_pages()</monospace> with DSA. We prototype a DSA-enabled Linux kernel and evaluate its effectiveness through two benchmarks demonstrating real-world page compaction (<monospace>kcompactd</monospace>) and promotion (<monospace>kdamond</monospace>) scenarios. In both cases, our prototype demonstrates improved throughput in page migration, benefiting both the kernel subsystem and applications.","PeriodicalId":51248,"journal":{"name":"IEEE Computer Architecture Letters","volume":"24 1","pages":"37-40"},"PeriodicalIF":1.4,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-14DOI: 10.1109/LCA.2025.3529213
E. Kritheesh;Biswabandan Panda
Last-level cache (LLC) covert-channels exploit the cache timing differences to transmit information. In recent works, the attacks rely on a single sender and a single receiver. Streamline is the state-of-the-art cache covert channel attack that uses a shared array of addresses mapped to the payload bits, allowing parallelization of the encoding and decoding of bits. As multi-core systems are ubiquitous, multiple senders and receivers can be used to create a high bandwidth cache covert channel. However, this is not the case, and the bandwidth per thread is limited by various factors. We extend Streamline to a multi-threaded Streamline, where the senders buffer a few thousand bits at the LLC for the receivers to decode. We observe that these buffered bits are prone to eviction by the co-running processes before they are decoded. We propose SPAM, a multi-threaded covert-channel at the LLC. SPAM shows that fewer but faster senders must encode for more receivers to reduce this time frame. This ensures resilience to noise coming from cache activities of co-running applications. SPAM uses two different access patterns for the sender(s) and the receiver(s). The sender access pattern of the addresses is modified to leverage the hardware prefetchers to accelerate the loads while encoding. The receiver access pattern circumvents the hardware prefetchers for accurate load latency measurements. We demonstrate SPAM on a six-core (12-threaded) system, achieving a bit-rate of 12.21 MB/s at an error rate of 9.02% which is an improvement of over 70% over the state-of-the-art multi-threaded Streamline for comparable error rates when 50% of the co-running threads stress the cache system.
{"title":"SPAM: Streamlined Prefetcher-Aware Multi-Threaded Cache Covert-Channel Attack","authors":"E. Kritheesh;Biswabandan Panda","doi":"10.1109/LCA.2025.3529213","DOIUrl":"https://doi.org/10.1109/LCA.2025.3529213","url":null,"abstract":"Last-level cache (LLC) covert-channels exploit the cache timing differences to transmit information. In recent works, the attacks rely on a single sender and a single receiver. Streamline is the state-of-the-art cache covert channel attack that uses a shared array of addresses mapped to the payload bits, allowing parallelization of the encoding and decoding of bits. As multi-core systems are ubiquitous, multiple senders and receivers can be used to create a high bandwidth cache covert channel. However, this is not the case, and the bandwidth per thread is limited by various factors. We extend Streamline to a multi-threaded Streamline, where the senders buffer a few thousand bits at the LLC for the receivers to decode. We observe that these buffered bits are prone to eviction by the co-running processes before they are decoded. We propose SPAM, a multi-threaded covert-channel at the LLC. SPAM shows that fewer but faster senders must encode for more receivers to reduce this time frame. This ensures resilience to noise coming from cache activities of co-running applications. SPAM uses two different access patterns for the sender(s) and the receiver(s). The sender access pattern of the addresses is modified to leverage the hardware prefetchers to accelerate the loads while encoding. The receiver access pattern circumvents the hardware prefetchers for accurate load latency measurements. We demonstrate SPAM on a six-core (12-threaded) system, achieving a bit-rate of 12.21 MB/s at an error rate of 9.02% which is an improvement of over 70% over the state-of-the-art multi-threaded Streamline for comparable error rates when 50% of the co-running threads stress the cache system.","PeriodicalId":51248,"journal":{"name":"IEEE Computer Architecture Letters","volume":"24 1","pages":"25-28"},"PeriodicalIF":1.4,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-09DOI: 10.1109/LCA.2025.3527458
Houxiang Ji;Minho Kim;Seonmu Oh;Daehoon Kim;Nam Sung Kim
Memory deduplication plays a critical role in reducing memory consumption and the total cost of ownership (TCO) in hyperscalers, particularly as the advent of large language models imposes unprecedented demands on memory resources. However, conventional CPU-based memory deduplication can interfere with co-running applications, significantly impacting the performance of time-sensitive workloads. Intel introduced the on-chip Data Streaming Accelerator (DSA), providing high-performance data movement and transformation capabilities, including comparison and checksum calculation, which are heavily utilized in the deduplication. In this work, we enhance a widely-used kernel-space memory deduplication feature, Kernel Samepage Merging (ksm), by selectively offloading these operations to the DSA. Our evaluation demonstrates that CPU-based ksm can lead to 5.0–10.9× increase in the tail latency of co-running applications while DSA-based ksm limits the latency increase to just 1.6× while achieving comparable memory savings.
{"title":"Cooperative Memory Deduplication With Intel Data Streaming Accelerator","authors":"Houxiang Ji;Minho Kim;Seonmu Oh;Daehoon Kim;Nam Sung Kim","doi":"10.1109/LCA.2025.3527458","DOIUrl":"https://doi.org/10.1109/LCA.2025.3527458","url":null,"abstract":"Memory deduplication plays a critical role in reducing memory consumption and the total cost of ownership (TCO) in hyperscalers, particularly as the advent of large language models imposes unprecedented demands on memory resources. However, conventional CPU-based memory deduplication can interfere with co-running applications, significantly impacting the performance of time-sensitive workloads. Intel introduced the <italic>on-chip</i> Data Streaming Accelerator (DSA), providing high-performance data movement and transformation capabilities, including comparison and checksum calculation, which are heavily utilized in the deduplication. In this work, we enhance a widely-used kernel-space memory deduplication feature, Kernel Samepage Merging (<monospace>ksm</monospace>), by selectively offloading these operations to the DSA. Our evaluation demonstrates that CPU-based <monospace>ksm</monospace> can lead to 5.0–10.9× increase in the tail latency of co-running applications while DSA-based <monospace>ksm</monospace> limits the latency increase to just 1.6× while achieving comparable memory savings.","PeriodicalId":51248,"journal":{"name":"IEEE Computer Architecture Letters","volume":"24 1","pages":"29-32"},"PeriodicalIF":1.4,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-08DOI: 10.1109/LCA.2025.3525970
Vardhana M;Rohan Pinto
Convolutional Neural Networks are deployed mostly on GPUs or CPUs. However, due to the increasing complexity of architecture and growing performance requirements, these platforms may not be suitable for deploying inference engines. ASIC and FPGA implementations are appearing as superior alternatives to software-based solutions for achieving the required performance. In this article, an efficient architecture for accelerating convolution using the Winograd transform is proposed and implemented on FPGA. The proposed accelerator consumes 38% less resources as compared with conventional GEMM-based implementation. Analysis results indicate that our accelerator can achieve 3.5 TOP/s, 1.28 TOP/s, and 1.42 TOP/s for VGG16, ResNet18, and MobileNetV2 CNNs, respectively, at 250 MHz. The proposed accelerator demonstrates the best energy efficiency as compared with prior arts.
{"title":"High-Performance Winograd Based Accelerator Architecture for Convolutional Neural Network","authors":"Vardhana M;Rohan Pinto","doi":"10.1109/LCA.2025.3525970","DOIUrl":"https://doi.org/10.1109/LCA.2025.3525970","url":null,"abstract":"Convolutional Neural Networks are deployed mostly on GPUs or CPUs. However, due to the increasing complexity of architecture and growing performance requirements, these platforms may not be suitable for deploying inference engines. ASIC and FPGA implementations are appearing as superior alternatives to software-based solutions for achieving the required performance. In this article, an efficient architecture for accelerating convolution using the Winograd transform is proposed and implemented on FPGA. The proposed accelerator consumes 38% less resources as compared with conventional GEMM-based implementation. Analysis results indicate that our accelerator can achieve 3.5 TOP/s, 1.28 TOP/s, and 1.42 TOP/s for VGG16, ResNet18, and MobileNetV2 CNNs, respectively, at 250 MHz. The proposed accelerator demonstrates the best energy efficiency as compared with prior arts.","PeriodicalId":51248,"journal":{"name":"IEEE Computer Architecture Letters","volume":"24 1","pages":"21-24"},"PeriodicalIF":1.4,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This letter introduces PINSim, a user-friendly and flexible framework for simulating emerging smart vision sensors in the early design stages. PINSim enables the realization of integrated sensing and processing near and in the sensor, effectively addressing challenges such as data movement and power-hungry analog-to-digital converters. The framework offers a flexible interface and a wide range of design options for customizing the efficiency and accuracy of processing-near/in-sensor-based accelerators using a hierarchical structure. Its organization spans from the device level upward to the algorithm level. PINSim realizes instruction-accurate evaluation of circuit-level performance metrics. PINSim achieves over $25,000times$ speed-up compared to SPICE simulation with less than a 4.1% error rate on average. Furthermore, it supports both multilayer perceptron (MLP) and convolutional neural network (CNN) models, with limitations determined by IoT budget constraints. By facilitating the exploration and optimization of various design parameters, PiNSim empowers researchers and engineers to develop energy-efficient and high-performance smart vision sensors for a wide range of applications.
{"title":"PINSim: A Processing In- and Near-Sensor Simulator to Model Intelligent Vision Sensors","authors":"Sepehr Tabrizchi;Mehrdad Morsali;David Pan;Shaahin Angizi;Arman Roohi","doi":"10.1109/LCA.2024.3522777","DOIUrl":"https://doi.org/10.1109/LCA.2024.3522777","url":null,"abstract":"This letter introduces PINSim, a user-friendly and flexible framework for simulating emerging smart vision sensors in the early design stages. PINSim enables the realization of integrated sensing and processing near and in the sensor, effectively addressing challenges such as data movement and power-hungry analog-to-digital converters. The framework offers a flexible interface and a wide range of design options for customizing the efficiency and accuracy of processing-near/in-sensor-based accelerators using a hierarchical structure. Its organization spans from the device level upward to the algorithm level. PINSim realizes instruction-accurate evaluation of circuit-level performance metrics. PINSim achieves over <inline-formula><tex-math>$25,000times$</tex-math></inline-formula> speed-up compared to SPICE simulation with less than a 4.1% error rate on average. Furthermore, it supports both multilayer perceptron (MLP) and convolutional neural network (CNN) models, with limitations determined by IoT budget constraints. By facilitating the exploration and optimization of various design parameters, PiNSim empowers researchers and engineers to develop energy-efficient and high-performance smart vision sensors for a wide range of applications.","PeriodicalId":51248,"journal":{"name":"IEEE Computer Architecture Letters","volume":"24 1","pages":"17-20"},"PeriodicalIF":1.4,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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