A Memristive Neural Network Computing Engine using CMOS-Compatible Charge-Trap-Transistor (CTT)

09/19/2017 ∙ by Yuan Du, et al. ∙ 0

A memristive neural network computing engine based on CMOS-compatible charge-trap transistor (CTT) is proposed in this paper. CTT devices are used as analog multipliers. Compared to digital multipliers, CTT-based analog multipliers show dramatic area and power reduction (>100x). The proposed memristive computing engine is composed of a scalable CTT multiplier array and energy efficient analog-digital interfaces. Through implementing the sequential analog fabric (SAF), the mixed-signal of the engine interfaces are simplified and hardware overhead remains constant regardless of the size of the array. A proof-of-concept 784 by 784 CTT computing engine is implemented using TSMC 28nm CMOS technology and occupied 0.68mm2. It achieves 69.9 TOPS with 500 MHz clock frequency and consumes 14.8 mW. As an example, we utilize this computing engine to address a classic pattern recognition problem-classifying handwritten digits on MNIST database - and obtained a performance comparable to state-of-the-art fully connected neural networks using 8-bit fixed-point resolution.

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