Analog circuit implementation of neurons with multiply-accumulate and relu functions

Huang，Yucong1,2; Yang，Zhitao1,2; Zhu，Jianghan1,2; Ye，Terry Tao1,2,3

10.1145/3386263.3406941

2020-09-07

Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI

493-498

Although Artificial Neural Networks (ANNs) are inspired by biological neural systems, most of ANNs today are implemented with digital circuitry and use binary values in computation. In recent years, analog-based neuromorphic system has gained lots of attention as it provides a natural interface for brain-machine interaction. In this paper, we present analog designs of a complete neuron system, where the Multiply-Accumulate (MAC) and Rectified Linear Unit (ReLU) functions are all implemented in analog circuits. The design uses SMIC 55nm standard LP CMOS process node and operates at low supply voltage (1.2 V). The simulation results in SPECTRE demonstrate that the MAC's linear error is no more than 0.5% and total harmonic distortion (THD) is less than 1.6% when the inputs vary from peak (-10 µA) to peak (10 µA) at 10 MHz, the -3dB bandwidth is 288 MHz, the maximum power consumption is 540 µW and the static power consumption is 493 µW under 100MHz input signal frequency. More specifically, our design is resilient to the fluctuation of power supply, which helps to achieve high precision of computation.

Analog circuit MOSFET Neural network Neuromorphic computing

第一 ; 通讯

英语

EI

20203909229991

Timing circuits ; Brain computer interface ; Electric power utilization ; Analog circuits

Electric Power Systems:706.1 ; Electronic Circuits:713 ; Pulse Circuits:713.4 ; Computer Peripheral Equipment:722.2

2-s2.0-85091277247

Scopus

1.Southern University of Science and Technology,Shenzhen,China
2.Department of Electrical and Electronic Engineering,United States
3.University Key Laboratory of Advanced Wireless Communication of Guangdong Province,China

Huang，Yucong,Yang，Zhitao,Zhu，Jianghan,et al. Analog circuit implementation of neurons with multiply-accumulate and relu functions[C],2020:493-498.