This processor minimizes the popcount bottleneck, thereby maximizing neural network computational throughput. Additionally, it offers potential for performance ...

This processor minimizes the popcount bottleneck, thereby maximizing neural network computational throughput. Additionally, it offers potential for performance ...

Photonic Processor for Fully Discretized. Neural Networks. Jeff Anderson, Shuai Sun, Yousra Alkabani,. Volker Sorger, Tarek El-Ghazawi. The George Washington ...

A feature of fully automatic implementation and intelligent response is highly desirable for the multipurpose photonic signal processors. Here, we report and ...

Feb 26, 2024 · Using a fixed activation threshold of 0.5, L2ONN can continually learn at most 14 tasks occupying totally 96.3% photonic neuron connections, ...

Sep 22, 2023 · Typically, the NNs' application in photonics is related to the processing of large data sets, which is the case in optical communications, ...

The final goal of this paper is to investigate whether photonic reservoirs are viable to preprocess input data when coupled to DNNs, resulting in a better ...

Jan 5, 2023 · In this work, convolution based on time-wavelength plane stretching approach is implemented on a microcomb-driven chip-based photonic processing unit (PPU).

In this paper, we present a novel optical-domain BNN accelerator, named ROBIN, which intelligently integrates heterogeneous microring resonator optical devices.

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