|
| | | | |
| | | |
| Stat |
Members: 3645
Articles: 2'501'711
Articles rated: 2609
20 April 2024 |
|
| | | | |
|
Article overview
| |
|
|
Balancing Efficiency and Flexibility for DNN Acceleration via Temporal GPU-Systolic Array Integration | | Cong Guo
; Yangjie Zhou
; Jingwen Leng
; Yuhao Zhu
; Zidong Du
; Quan Chen
; Chao Li
; Minyi Guo
; Bin Yao
; | | Date: |
18 Feb 2020 | | Abstract: | The research interest in specialized hardware accelerators for deep neural
networks (DNN) spiked recently owing to their superior performance and
efficiency. However, today’s DNN accelerators primarily focus on accelerating
specific "kernels" such as convolution and matrix multiplication, which are
vital but only part of an end-to-end DNN-enabled application. Meaningful
speedups over the entire application often require supporting computations that
are, while massively parallel, ill-suited to DNN accelerators. Integrating a
general-purpose processor such as a CPU or a GPU incurs significant data
movement overhead and leads to resource under-utilization on the DNN
accelerators.
We propose Simultaneous Multi-mode Architecture (SMA), a novel architecture
design and execution model that offers general-purpose programmability on DNN
accelerators in order to accelerate end-to-end applications. The key to SMA is
the temporal integration of the systolic execution model with the GPU-like SIMD
execution model. The SMA exploits the common components shared between the
systolic-array accelerator and the GPU, and provides lightweight
reconfiguration capability to switch between the two modes in-situ. The SMA
achieves up to 63% performance improvement while consuming 23% less energy than
the baseline Volta architecture with TensorCore. | | Source: | arXiv, 2002.8326 | | Services: | Forum | Review | PDF | Favorites | |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
browser Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)
|
| |
|
|
|
|
News, job offers and information for researchers and scientists:
| |
REGISTER