학술논문

A Fully Differential 4-Bit Analog Compute-In-Memory Architecture for Inference Application
Document Type
Conference
Source
2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS) Artificial Intelligence Circuits and Systems (AICAS), 2023 IEEE 5th International Conference on. :1-5 Jun, 2023
Subject
Bioengineering
Components, Circuits, Devices and Systems
Computing and Processing
Signal Processing and Analysis
Semiconductor device modeling
Circuits and systems
Computational modeling
Linearity
Computer architecture
Voltage
Dynamic range
Analog
bit-line
compute-in-memory (CIM)
differential
energy-efficiency
multiplication and accumulate (MAC)
Language
ISSN
2834-9857
Abstract
A robust, fully differential multiplication and accumulate (MAC) scheme for analog compute-in-memory (CIM) architecture is proposed in this article. The proposed method achieves a high signal margin for 4-bit CIM architecture due to fully differential voltage changes on read bit-lines (RBL/RBLBs). The signal margin achieved for 4-bit MAC operation is 32 mV, which is 1.14×, 5.82×, and 10.24× higher than the state-of-the-art. The proposed scheme is robust against the process, voltage, and temperature (PVT) variations and achieves a variability metric (σ/µ) of 3.64 %, which is 2.36× and 2.66× lower than the reported works. The architecture has achieved an energy-efficiency of 2.53 TOPS/W at 1 V supply voltage in 65 nm CMOS technology, that is 6.2× efficient than digital baseline HW [25]. Furthermore, the inference accuracy of the architecture is 97.6% on the MNIST data set with a LeNet-5 CNN model. The figure-of-merit (FoM) of the proposed design is 355, which is 3.28×, 3.58×, and 17.75× higher than state-of-the-art.