Abstract
In the modern era of artificial intelligence, increasingly sophisticated
artificial neural networks (ANNs) are implemented, which pose challenges
in terms of execution speed and power consumption. To tackle this
problem, recent research on reduced-precision ANNs opened the
possibility to exploit analog hardware for neuromorphic acceleration. In
this scenario, photonic-electronic engines are emerging as a
short-medium term solution to exploit the high speed and inherent
parallelism of optics for linear computations needed in ANN, while
resorting to electronic circuitry for signal conditioning and memory
storage. In this paper we introduce a precision-scalable integrated
photonic-electronic multiply-accumulate neuron, namely PEMAN. The
proposed device relies on (i) an analog photonic engine to perform
reduced-precision multiplications at high speed and low power, and (ii)
an electronic front-end for accumulation and application of the
nonlinear activation function by means of a nonlinear encoding in the
analog-to-digital converter (ADC). The device, based on the iSiPP50G SOI
process for the photonic engine and a commercial 28 nm CMOS process for
the electronic front end, has been numerically validated through
cosimulations to perform multiply-accumulate operations (MAC). PEMAN
exhibits a multiplication accuracy of 6.1 ENOB up to 10 GMAC/s, while it
can perform computations up to 56 GMAC/s with a reduced accuracy down to
2.1 ENOB. The device can trade off speed with resolution and power
consumption, it outperforms its analog electronics counterparts both in
terms of speed and power consumption, and brings substantial
improvements also compared to a leading GPU.