LV: Latency-Versatile Floating-Point Engine for High-Performance Deep Neural Networks
Author:
Affiliation:
1. Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan
Funder
NSTC
MOE
Synopsys Fellowship
NSTC Fellowship
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Hardware and Architecture
Link
http://xplorestaging.ieee.org/ielx7/10208/10189818/10230255.pdf?arnumber=10230255
Reference8 articles.
1. Block and subword-scaling floating-point (BSFP) : An efficient non-uniform quantization for low precision inference;lo;Proc Int Conf Learn Representations,2023
2. A study of BFLOAT16 for deep learning training;kalamkar,2019
3. RASA: Efficient Register-Aware Systolic Array Matrix Engine for CPU
4. Pushing the limits of narrow precision inferencing at cloud scale with microsoft floating point;darvish rouhani;Proc Adv Neural Inf Process Syst,2020
5. 31.3 A Compute-Adaptive Elastic Clock-Chain Technique with Dynamic Timing Enhancement for 2D PE-Array-Based Accelerators
Cited by 1 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. Bucket Getter: A Bucket-based Processing Engine for Low-bit Block Floating Point (BFP) DNNs;56th Annual IEEE/ACM International Symposium on Microarchitecture;2023-10-28
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