AGNI: In-Situ, Iso-Latency Stochastic-to-Binary Number Conversion for In-DRAM Deep Learning

Author:

Shivanandamurthy Supreeth Mysore1,Vatsavai Sairam Sri1,Thakkar Ishan1,Salehi Sayed Ahmad1

Affiliation:

1. University of Kentucky,Department of Electrical and Computer Engineering,Lexington,KY,USA,40506

Publisher

IEEE

Reference27 articles.

1. 14.2 A Compute SRAM with Bit-Serial Integer/Floating-Point Operations for Programmable In-Memory Vector Acceleration

2. Conv-sram: An energy-efficient sram with in-memory dot-product computation for low-power convolutional neural networks;biswas;IEEE JSSC,2018

3. Roles and Missions for National Changgeuk Company of Korea to Eatablish Universal Korean Music Theatres: Mainly on Changgeuks, Chung and Romeo And Juliet

4. XNOR-SRAM: In-Memory Computing SRAM Macro for Binary/Ternary Deep Neural Networks

5. Lacc: Exploiting lookup table-based fast and accurate vector multiplication in dram-based cnn accelerator;den;IEEE DAC,2019

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