More Like this

1. Silicon Photonics for Artificial Intelligence and Neuromorphic Computing

   https://doi.org/10.1109/SUM48717.2021.9505837  

   Shastri, Bhavin J.; de Lima, Thomas Ferreira; Huang, Chaoran; Marquez, Bicky A.; Shekhar, Sudip; Chrostowski, Lukas; Prucnal, Paul R. (July 2021, in 2021 IEEE Photonics Society Summer Topicals Meeting Series (SUM))

   null (Ed.)

   Artificial intelligence and neuromorphic computing driven by neural networks has enabled many applications. Software implementations of neural networks on electronic platforms are limited in speed and energy efficiency. Neuromorphic photonics aims to build processors in which optical hardware mimic neural networks in the brain. 

   more » « less
2. Silicon photonics for artificial intelligence applications

   https://doi.org/10.1051/photon/202010440  

   Marquez, Bicky A.; Filipovich, Matthew J.; Howard, Emma R.; Bangari, Viraj; Guo, Zhimu; Morison, Hugh D.; Ferreira De Lima, Thomas; Tait, Alexander N.; Prucnal, Paul R.; Shastri, Bhavin J. (September 2020, Photoniques)

   null (Ed.)

   Artificial intelligence enabled by neural networks has enabled applications in many fields (e.g. medicine, finance, autonomous vehicles). Software implementations of neural networks on conventional computers are limited in speed and energy efficiency. Neuromorphic engineering aims to build processors in which hardware mimic neurons and synapses in brain for distributed and parallel processing. Neuromorphic engineering enabled by silicon photonics can offer subnanosecond latencies, and can extend the domain of artificial intelligence applications to high-performance computing and ultrafast learning. We discuss current progress and challenges on these demonstrations to scale to practical systems for training and inference. 

   more » « less
3. Approximating Back-propagation for a Biologically Plausible Local Learning Rule in Spiking Neural Networks

   https://doi.org/10.1145/3354265.3354275  

   Shrestha, Amar; Fang, Haowen; Wu, Qing; Qiu, Qinru (July 2019, International Conference on Neuromorphic Systems)

   Asynchronous event-driven computation and communication using spikes facilitate the realization of spiking neural networks (SNN) to be massively parallel, extremely energy efficient and highly robust on specialized neuromorphic hardware. However, the lack of a unified robust learning algorithm limits the SNN to shallow networks with low accuracies. Artificial neural networks (ANN), however, have the backpropagation algorithm which can utilize gradient descent to train networks which are locally robust universal function approximators. But backpropagation algorithm is neither biologically plausible nor neuromorphic implementation friendly because it requires: 1) separate backward and forward passes, 2) differentiable neurons, 3) high-precision propagated errors, 4) coherent copy of weight matrices at feedforward weights and the backward pass, and 5) non-local weight update. Thus, we propose an approximation of the backpropagation algorithm completely with spiking neurons and extend it to a local weight update rule which resembles a biologically plausible learning rule spike-timing-dependent plasticity (STDP). This will enable error propagation through spiking neurons for a more biologically plausible and neuromorphic implementation friendly backpropagation algorithm for SNNs. We test the proposed algorithm on various traditional and non-traditional benchmarks with competitive results. 

   more » « less
4. A Superconducting Nanowire-based Architecture for Neuromorphic Computing. , Selected as a Highlight of NCE for 2022.

   Lombo, Andres E.; Lares, Jesus E.; Castellani, Matteo; Chou, Chi-Ning; Lynch, Nancy; Berggren, Karl K. (January 2022, Neuromorphic computing and engineering)

   Neuromorphic computing would benefit from the utilization of improved customized hardware. However, the translation of neuromorphic algorithms to hardware is not easily accomplished. In particular, building superconducting neuromorphic systems requires expertise in both superconducting physics and theoretical neuroscience, which makes such design particularly challenging. In this work, we aim to bridge this gap by presenting a tool and methodology to translate algorithmic parameters into circuit specifications. We first show the correspondence between theoretical neuroscience models and the dynamics of our circuit topologies. We then apply this tool to solve a linear system and implement Boolean logic gates by creating spiking neural networks with our superconducting nanowire-based hardware. 

   more » « less
5. A Superconducting Nanowire-based Architecture for Neuromorphic Computing

   https://doi.org/10.1088/2634-4386/ac86ef  

   Lombo, Andrews E.; Lares, Jesus E.; Castellani, Matteo; Chou, Chi-Ning; Lynch, Nancy; Berggren, Karl K. (January 2022, Neuromorphic computing and engineering)

   Neuromorphic computing would benefit from the utilization of improved customized hardware. However, the translation of neuromorphic algorithms to hardware is not easily accomplished. In particular, building superconducting neuromorphic systems requires expertise in both supercon- ducting physics and theoretical neuroscience, which makes such design particularly challenging. In this work, we aim to bridge this gap by presenting a tool and methodology to translate algorith- mic parameters into circuit specifications. We first show the correspondence between theoretical neuroscience models and the dynamics of our circuit topologies. We then apply this tool to solve a linear system and implement Boolean logic gates by creating spiking neural networks with our superconducting nanowire-based hardware. 

   more » « less