CNS Core: Small: Toward Opportunistic, Fast, and Robust In-Cache AI Acceleration at the Edge

CNS 核心：小型：在边缘实现机会主义、快速且稳健的缓存内 AI 加速

• 批准号: 2228028
• 负责人: Shaahin Angizi
• 金额: $ 29.92万
• 依托单位: New Jersey Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2228028&HistoricalAwards=false
• 关键词: CNS; Core; Small; Toward; Opportunistic

Internet of Things (IoT) is anticipated to comprise approximately 75+ billion interconnected devices by 2025. Many Artificial Intelligence (AI)-enabled IoT devices consist of sensory imaging systems that enable data collection from people and the environment. However, insufficient computing ability of small IoT devices such as smartphones, wearable devices, etc., and memory/compute-intensive AI tasks prevent AI techniques from being widely deployed in such devices. This proposal enables a smooth transition from the state-of-the-art cloud-centric IoT approach to a data-centric approach, enabling mobile edge devices to perform computation close to the sensor by repurposing the cache memory to a data-parallel processing unit. This will remarkably reduce the power consumption and latency of data transmission to the cloud. Moreover, this project seeks to design and deploy new hardware-oriented AI algorithms into edge devices for efficient image processing, reducing the computation complexity and memory access, while maintaining accuracy. With the technologies developed in this project, more powerful and stable IoT devices can be introduced ensuring accelerated operation with applications of societal importance spanning healthcare monitoring, automotive applications, industrial and agriculture sensing, intelligent infrastructure, etc. A comprehensive circuit-to-system assessment framework will be adopted to systematically evaluate the performance of the system on several IoT workload suites. This project will make a strong effort on developing undergraduate and graduate course modules, propagating transportable and open-source models, and broadening STEM participation through publications/presentations at conferences and workshops and involving undergraduate minority students.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

预计到 2025 年，物联网 (IoT) 将包含约 75 亿多个互连设备。许多支持人工智能 (AI) 的物联网设备都包含传感成像系统，可以从人和环境中收集数据。然而，智能手机、可穿戴设备等小型物联网设备的计算能力不足，以及内存/计算密集型人工智能任务阻碍了人工智能技术在此类设备中的广泛部署。该提案实现了从最先进的以云为中心的物联网方法到以数据为中心的方法的平稳过渡，使移动边缘设备能够通过将缓存重新调整为数据并行处理单元来执行靠近传感器的计算。这将显着降低数据传输到云端的功耗和延迟。此外，该项目寻求设计新的面向硬件的人工智能算法并将其部署到边缘设备中，以实现高效的图像处理，降低计算复杂性和内存访问，同时保持准确性。 借助该项目开发的技术，可以引入更强大、更稳定的物联网设备，确保加速运行具有社会重要性的应用，涵盖医疗保健监控、汽车应用、工业和农业传感、智能基础设施等。全面的电路到系统评估将采用框架来系统地评估系统在多个物联网工作负载套件上的性能。 该项目将大力开发本科生和研究生课程模块，传播可移植和开源模型，并通过在会议和研讨会上发表出版物/演讲以及让少数族裔本科生参与来扩大 STEM 参与。该奖项反映了 NSF 的法定使命，并已通过使用基金会的智力优点和更广泛的影响审查标准进行评估，认为值得支持。

项目成果

AppCiP: Energy-Efficient Approximate Convolution-in-Pixel Scheme for Neural Network Acceleration

• DOI: 10.1109/jetcas.2023.3242167
• 发表时间: 2023-03
• 期刊: IEEE Journal on Emerging and Selected Topics in Circuits and Systems
• 影响因子: 4.6
• 作者: Sepehr Tabrizchi;Ali Nezhadi;Shaahin Angizi;A. Roohi

Accelerating Low Bit-width Neural Networks at the Edge, PIM or FPGA: A Comparative Study

• DOI: 10.1145/3583781.3590213
• 发表时间: 2023-06
• 期刊: Proceedings of the Great Lakes Symposium on VLSI 2023
• 作者: Nakul Kochar;Lucas Ekiert;Deniz Najafi;Deliang Fan;Shaahin Angizi

LT-PIM: An LUT-Based Processing-in-DRAM Architecture With RowHammer Self-Tracking

LT-PIM：具有 RowHammer 自跟踪功能的基于 LUT 的 DRAM 处理架构

• DOI: 10.1109/lca.2022.3220084
• 发表时间: 2022
• 期刊: IEEE Computer Architecture Letters
• 影响因子: 2.3
• 作者: Zhou, Ranyang;Tabrizchi, Sepehr;Roohi, Arman;Angizi, Shaahin
• 通讯作者: Angizi, Shaahin

A Near-Sensor Processing Accelerator for Approximate Local Binary Pattern Networks

• DOI: 10.1109/tetc.2023.3285493
• 发表时间: 2022-10
• 期刊: IEEE Transactions on Emerging Topics in Computing
• 影响因子: 5.9
• 作者: Shaahin Angizi;Mehrdad Morsali;Sepehr Tabrizchi;A. Roohi

P-PIM: A Parallel Processing-in-DRAM Framework Enabling Row Hammer Protection

P-PIM：支持行锤保护的 DRAM 并行处理框架

• DOI: 10.23919/date56975.2023.10137204
• 发表时间: 2023
• 期刊: Automation & Test in Europe Conference (DATE 2023
• 作者: Zhou, Ranyang;Tabrizchi, Sepehr;Morsali, Mehrdad;Roohi, Arman;Angizi, Shaahin
• 通讯作者: Angizi, Shaahin