CNS Core: Small: AccelRITE: Accelerating ReInforcemenT Learning based AI at the Edge Using FPGAs

CNS 核心：小型：AccelRITE：使用 FPGA 在边缘加速基于强化学习的 AI

基本信息

批准号：
2009057
负责人：
Viktor Prasanna
金额：
$ 49.97万
依托单位：
University of Southern California
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2009057&HistoricalAwards=false
关键词：
CNS Core Small AccelRITE Accelerating

项目摘要

Artificial Intelligence (AI) has led to significant progress in several domains such as self-driving cars and robotics. Reinforcement Learning (RL) is a class of AI that includes algorithms that enable machines to teach themselves optimal decision making. However, RL algorithms are complex and time-consuming, which render them unsuitable for applications that require fast response. Heterogeneous platforms, which couple a Central Processing Unit (CPU) with an integrated circuit that can be configured - Field Programmable Gate Arrays (FPGA) are promising candidates for implementing fast algorithms due to their capabilities. The project will develop fast implementations of RL algorithms targeting such platforms. The intellectual merits of the project include the research and development of innovative optimizations that exploit the heterogeneity of the emerging class of FPGA devices and address challenges such as conflicts in parallel accesses to shared objects, irregular memory accesses, and overheads in fine grained acceleration. The project will develop parameterized performance models for key AI kernels – Stochastic Gradient Descent (SGD), conjugate gradient, parallel hash tables, and neural networks, to enable energy-performance trade-off analysis. The proposed project will develop a novel spatiotemporal constraint graph-based design space exploration technique to accelerate RL algorithms by taking a holistic view of the algorithm.The broader impact of the project is in efficient use of heterogeneous architectures consisting of CPUs and FPGAs coupled with cache coherent memory for accelerating AI for edge computing. Successful completion of this project will lead to significant increase in the complexity of AI applications that can be deployed in real world environments. This will lead to a dramatic improvement in the capabilities of AI enabled devices such as self-driving cars, robotics, and wearable healthcare devices. The project will also constitute materials appropriate for inclusion in graduate and undergraduate courses.All software developed in the project will be posted on github at: https://github.com/pgroupATusc. Software releases will be maintained for a period of not less than 3 years after the conclusion of the grant.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.

人工智能 (AI) 在自动驾驶汽车和机器人等多个领域取得了重大进展，强化学习 (RL) 是一类人工智能，其中包括使机器能够自学最佳决策的算法。复杂且耗时，这使得它们不适合需要快速响应的应用。异构平台将中央处理单元（CPU）与可配置的集成电路相结合 - 现场可编程门阵列（FPGA）很有前途。该项目将开发针对此类平台的 RL 算法的快速实现，该项目的智力优势包括研究和开发利用新兴 FPGA 设备的异构性并解决问题的创新优化。该项目将为关键人工智能内核开发参数化性能模型——随机梯度下降（SGD）、共轭梯度、并行哈希等挑战。该项目将开发一种新颖的基于时空约束图的设计空间探索技术，通过整体看待算法来加速强化学习算法。该项目有效利用由 CPU 和 FPGA 组成的异构架构以及高速缓存一致性内存来加速边缘计算的人工智能，该项目的成功完成将导致可在现实世界环境中部署的人工智能应用程序的复杂性显着增加。这将导致该项目还将构建适合研究生和本科生课程的材料。该项目开发的所有软件都将发布在github 网址：https://github.com/pgroupATusc。软件版本将在资助结束后保留不少于 3 年。该奖项反映了 NSF 的法定使命，并通过评估认为值得支持。基金会的智力价值以及更广泛的影响审查标准。

项目成果

期刊论文数量（22）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

DYNAMAP: Dynamic Algorithm Mapping Framework for Low Latency CNN Inference

DOI：
10.1145/3431920.3439286
发表时间：
2020-12
期刊：
The 2021 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays
影响因子：
0
作者：
Yuan Meng;S. Kuppannagari;R. Kannan;V. Prasanna
通讯作者：
Yuan Meng;S. Kuppannagari;R. Kannan;V. Prasanna

A Framework for Mapping DRL Algorithms With Prioritized Replay Buffer Onto Heterogeneous Platforms