6T-8T Hybrid SRAM for Lower-Power Neural-Network Processing by Lowering Operating Voltage

This letter introduces an innovation for the heterogeneous storage architecture of AI chips, specifically focusing on the integration of six transistors(6T) and eight transistors(8T) hybrid SRAM. Traditional approaches to reducing SRAM power consumption typically involve lowering the operating voltage, a method that often substantially diminishes the recognition rate of neural networks. However, the innovative design detailed in this letter amalgamates the strengths of both SRAM types. It operates at a voltage lower than conventional SRAM, thereby significantly reducing the power consumption in neural networks without compromising performance.

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Publication Details

Journal
IEICE Transactions on Information and Systems
Published
2024-08-31
DOI
https://doi.org/10.1587/transinf.2024edl8022
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00
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article

6T-8T Hybrid SRAM for Lower-Power Neural-Network Processing by Lowering Operating Voltage

Kazuteru Namba, Ji Wu, Ruoxi YU
IEICE Transactions on Information and Systems
Advanced Memory and Neural Computing
article

6T-8T Hybrid SRAM for Lower-Power Neural-Network Processing by Lowering Operating Voltage

Kazuteru Namba, Ji Wu, Ruoxi YU
article en

Abstract

This letter introduces an innovation for the heterogeneous storage architecture of AI chips, specifically focusing on the integration of six transistors(6T) and eight transistors(8T) hybrid SRAM. Traditional approaches to reducing SRAM power consumption typically involve lowering the operating voltage, a method that often substantially diminishes the recognition rate of neural networks. However, the innovative design detailed in this letter amalgamates the strengths of both SRAM types. It operates at a voltage lower than conventional SRAM, thereby significantly reducing the power consumption in neural networks without compromising performance.

IEICE Transactions on Information and SystemsVol. E107.D(9)
Chiba University (JP)
Affordable and clean energy
Openalex Percentile: Top 88%
Advanced Memory and Neural Computing
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