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A Codesigned Integrated Photonic Electronic Neuron
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  • Lorenzo De Marinis ,
  • Alessandro Catania ,
  • Piero Castoldi ,
  • Giampiero Contestabile ,
  • Paolo Bruschi ,
  • Massimo Piotto ,
  • Nicola Andriolli
Lorenzo De Marinis
Scuola Superiore SantAnna, Scuola Superiore SantAnna

Corresponding Author:[email protected]

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Alessandro Catania
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Piero Castoldi
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Giampiero Contestabile
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Paolo Bruschi
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Massimo Piotto
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Nicola Andriolli
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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.
Oct 2022Published in IEEE Journal of Quantum Electronics volume 58 issue 5 on pages 1-10. 10.1109/JQE.2022.3177793