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C3PU: Cross-Coupling Capacitor Processing Unit Using Analog-Mixed Signal In-Memory Computing for AI Inference

arXiv 2110.05947 ↗First posted 11 Oct 2021cs.LG cs.AI cs.ETcited by 0

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Abstract (arXiv)

This paper presents a novel cross-coupling capacitor processing unit (C3PU) that supports analog-mixed signal in memory computing to perform multiply-and-accumulate (MAC) operations. The C3PU consists of a capacitive unit, a CMOS transistor, and a voltage-to-time converter (VTC). The capacitive unit serves as a computational element that holds the multiplier operand and performs multiplication once the multiplicand is applied at the terminal. The multiplicand is the input voltage that is converted to a pulse width signal using a low power VTC. The transistor transfers this multiplication where a voltage level is generated. A demonstrator of 5x4 C3PU array that is capable of implementing 4 MAC units is presented. The design has been verified using Monte Carlo simulation in 65 nm technology. The 5x4 C3PU consumed energy of 66.4 fJ/MAC at 0.3 V voltage supply with an error of 5.7%. The proposed unit achieves lower energy and occupies a smaller area by 3.4x and 3.6x, respectively, with similar error value when compared to a digital-based 8x4-bit fixed point MAC unit. The C3PU has been utilized through an iris fower classification utilizing an artificial neural network which achieved a 90% classification accuracy compared to ideal accuracy of 96.67% using MATLAB.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 25 references that are in the map. First posted on arXiv 11 Oct 2021.

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References · 2

  1. 2016EIE: Efficient Inference Engine on Compressed Deep Neural Networkcited by 295
  2. 2017Analog CMOS-based Resistive Processing Unit for Deep Neural Network Trainingcited by 3

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