A 639.38GOPS Hybrid-Domain Logic-Compatible Multi-Level Embedded Flash Computing-in-Memory Macro with Spike-Edge Computation

Computing in Memory (CIM) has emerged as a promising research field, significantly reducing power consumption and latency associated with massive data movement and multiply-and-accumulate (MAC) operations in edge Al devices. For battery-constrained edge devices, non-volatile memories (NVMs) are ideal due to their ability to retain neural network parameters even when powered down, ensuring rapid response time upon device wake-up. Recent studies [2, 3] have integrated NVM into CIM architectures using STT-MRAM and RRAM, achieving high throughput and energy efficiency. Nevertheless, NVMs necessitate specialized fabrication technologies and additional manufacturing steps, which increase process complexity and costs (Fig. 1 (top-left)). As NVM processes advance, these technologies become increasingly challenging to access due to the escalating costs.

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Published
2024-11-18
DOI
https://doi.org/10.1109/a-sscc60305.2024.10848661
Primary Topic
Advanced Data Storage Technologies
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article
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article

A 639.38GOPS Hybrid-Domain Logic-Compatible Multi-Level Embedded Flash Computing-in-Memory Macro with Spike-Edge Computation

Minkyu Je, Sohmyung Ha, Ik‐Joon Chang, Bo‐Ran Choi et al.
Advanced Data Storage Technologies
article

A 639.38GOPS Hybrid-Domain Logic-Compatible Multi-Level Embedded Flash Computing-in-Memory Macro with Spike-Edge Computation

Minkyu Je, Sohmyung Ha, Ik‐Joon Chang, Bo‐Ran Choi, Edward Choi, Donghyeon Yi, Jiho Chun
article en

Abstract

Computing in Memory (CIM) has emerged as a promising research field, significantly reducing power consumption and latency associated with massive data movement and multiply-and-accumulate (MAC) operations in edge Al devices. For battery-constrained edge devices, non-volatile memories (NVMs) are ideal due to their ability to retain neural network parameters even when powered down, ensuring rapid response time upon device wake-up. Recent studies [2, 3] have integrated NVM into CIM architectures using STT-MRAM and RRAM, achieving high throughput and energy efficiency. Nevertheless, NVMs necessitate specialized fabrication technologies and additional manufacturing steps, which increase process complexity and costs (Fig. 1 (top-left)). As NVM processes advance, these technologies become increasingly challenging to access due to the escalating costs.

New York University Abu Dhabi (AE), Korea Advanced Institute of Science and Technology (KR), Kyung Hee University (KR)
Affordable and clean energy
Openalex Percentile: Top 69%
Advanced Data Storage Technologies
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