Affiliation:
1. Technion-Israel Institute of Technology, Haifa, Israel
2. University of Utah, Salt Lake City, Utah
Abstract
Computationally intensive neural network applications often need to run on resource-limited low-power devices. Numerous hardware accelerators have been developed to speed up the performance of neural network applications and reduce power consumption; however, most focus on data centers and full-fledged systems. Acceleration in ultra-low-power systems has been only partially addressed. In this article, we present multiPULPly, an accelerator that integrates memristive technologies within standard low-power CMOS technology, to accelerate multiplication in neural network inference on ultra-low-power systems. This accelerator was designated for PULP, an open-source microcontroller system that uses low-power RISC-V processors. Memristors were integrated into the accelerator to enable power consumption only when the memory is active, to continue the task with no context-restoring overhead, and to enable highly parallel analog multiplication. To reduce the energy consumption, we propose novel dataflows that handle common multiplication scenarios and are tailored for our architecture. The accelerator was tested on FPGA and achieved a peak energy efficiency of 19.5 TOPS/W, outperforming state-of-the-art accelerators by 1.5× to 4.5×.
Publisher
Association for Computing Machinery (ACM)
Subject
Electrical and Electronic Engineering,Hardware and Architecture,Software
Reference82 articles.
1. GAP9. 2021. Retrieved from https://greenwaves-technologies.com/gap9_iot_application_processor. GAP9. 2021. Retrieved from https://greenwaves-technologies.com/gap9_iot_application_processor.
2. Pulp Platform Website. 2021. Retrieved from https://www.pulp-platform.org. Pulp Platform Website. 2021. Retrieved from https://www.pulp-platform.org.
3. YAML. 2011. Retrieved from https://yaml.org. YAML. 2011. Retrieved from https://yaml.org.
4. stm32h743 datasheet.2019. https://www.st.com/resource/en/datasheet/stm32l476je.pdf. stm32h743 datasheet.2019. https://www.st.com/resource/en/datasheet/stm32l476je.pdf.
5. Resistive Random Access Memory (ReRAM) Based on Metal Oxides
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Accelerators in Embedded Systems for Machine Learning: A RISCV View;2023 38th Conference on Design of Circuits and Integrated Systems (DCIS);2023-11-15
2. A CNN Hardware Accelerator Using Triangle-based Convolution;ACM Journal on Emerging Technologies in Computing Systems;2022-10-13
3. A review of CNN accelerators for embedded systems based on RISC-V;2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS);2022-08-01
4. A Parallel SystemC Virtual Platform for Neuromorphic Architectures;2022 23rd International Symposium on Quality Electronic Design (ISQED);2022-04-06