GEBA: Gradient-Error-Based Approximation of Activation Functions
Author:
Affiliation:
1. Division of Materials Science and Engineering, Hanyang University, Seoul, Republic of Korea
2. Division of Materials Science and Engineering and the Department of Semiconductor Engineering, Hanyang University, Seoul, Republic of Korea
Funder
National Research and Development Program through the National Research Foundation of Korea
Ministry of Science and ICT
Institute of Information & Communications Technology Planning & Evaluation
Korea Government
IITP through the Artificial Intelligence Semiconductor Support Program to nurture the best talents
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/5503868/10375869/10302226.pdf?arnumber=10302226
Reference26 articles.
1. 16.3 A 28nm 384kb 6T-SRAM Computation-in-Memory Macro with 8b Precision for AI Edge Chips
2. 25.4 A 20nm 6GB Function-In-Memory DRAM, Based on HBM2 with a 1.2TFLOPS Programmable Computing Unit Using Bank-Level Parallelism, for Machine Learning Applications
3. A 1ynm 1.25V 8Gb, 16Gb/s/pin GDDR6-based Accelerator-in-Memory supporting 1TFLOPS MAC Operation and Various Activation Functions for Deep-Learning Applications
4. Optimal Weight‐Splitting in Resistive Random Access Memory‐Based Computing‐in‐Memory Macros
5. In-Datacenter Performance Analysis of a Tensor Processing Unit
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