Year: 2024
Type: article
Abstract: The traditional Von Neumann architecture creates bottlenecks due to data movement. The compute-in-memory (CIM) architecture performs computations within memory bit-cell arrays, enhancing computational performance. Edge devices utilizing artificial intelligence (AI) address real-time problems and have established themselves as groundbreaking technology. The 8T structure proposed in this paper has strengths over other existing structures in that it better withstands environmental changes within the SRAM and consumes lower power during memory operation. This structure minimizes reliance on complex ADCs, instead utilizing a simplified voltage differential approach for multiply-and-accumulate (MAC) operations, which enhances both power efficiency and stability. Based on these strengths, it can achieve higher battery efficiency in AI edge devices and improve system performance. The proposed integrated circuit was simulated in a 90 nm CMOS process and operated on a 1 V supply voltage.
Source: Applied Sciences
DOI: 10.3390/app142310924
Authors: Hye-Ju Shin, Sung-Hun Jo
Institution: Tech University of Korea
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,728
OpenAlex ID: W4404694195