Efficient Hardware Acceleration of Sparsely Active Convolutional Spiking Neural Networks

Sommer, J.B.; Ozkan, M. Akif; Keszöcze, Oliver; Teich, Jürgen

doi:10.1109/tcad.2022.3197512

Cited by 14 publications

(6 citation statements)

References 36 publications

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“…This combination is very powerful, as it harnesses the power of a convolutional filter alongside the spiking mechanism of IF or LIF neurons. When using low-power FPGA boards, as in several studies [ 49 , 51 , 52 , 53 , 54 , 55 , 56 ], it is challenging to balance both the deeper convolutions and spiking mechanisms. The current study was able to overcome several of the challenges faced by other works because of the following: We hosted deeper convolutions alongside SNNs with very few parameters compared to [ 49 ] and were still able to achieve similar accuracy over the MNIST and CIFAR10 datasets.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The current study was able to overcome several of the challenges faced by other works because of the following: We hosted deeper convolutions alongside SNNs with very few parameters compared to [ 49 ] and were still able to achieve similar accuracy over the MNIST and CIFAR10 datasets. We employed both real-valued and Poisson distribution spikes as input encoding schemes to capture most of the information before processing them through DCSNNs, which were not used in [ 49 , 51 , 52 , 53 , 54 , 55 , 56 ]. We tested the DCSNNs on automotive relevant datasets such as KITTI, INHA_ADAS, and INHA_KLP as opposed to just MNIST and CIFAR10, as was the case in [ 49 , 52 , 54 , 55 , 56 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“… We employed both real-valued and Poisson distribution spikes as input encoding schemes to capture most of the information before processing them through DCSNNs, which were not used in [ 49 , 51 , 52 , 53 , 54 , 55 , 56 ]. We tested the DCSNNs on automotive relevant datasets such as KITTI, INHA_ADAS, and INHA_KLP as opposed to just MNIST and CIFAR10, as was the case in [ 49 , 52 , 54 , 55 , 56 ]. We customized the proposed SGD-BP to fit the low-power needs of several target medium-sized intelligent vehicle industries in the form of FPGA implementation while preserving accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

“…We tested the DCSNNs on automotive relevant datasets such as KITTI, INHA_ADAS, and INHA_KLP as opposed to just MNIST and CIFAR10, as was the case in [ 49 , 52 , 54 , 55 , 56 ].…”

Section: Literature Reviewmentioning

confidence: 99%

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Implementation of Field-Programmable Gate Array Platform for Object Classification Tasks Using Spike-Based Backpropagated Deep Convolutional Spiking Neural Networks

Kakani

Cui

et al. 2023

Micromachines

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This paper investigates the performance of deep convolutional spiking neural networks (DCSNNs) trained using spike-based backpropagation techniques. Specifically, the study examined temporal spike sequence learning via backpropagation (TSSL-BP) and surrogate gradient descent via backpropagation (SGD-BP) as effective techniques for training DCSNNs on the field programmable gate array (FPGA) platform for object classification tasks. The primary objective of this experimental study was twofold: (i) to determine the most effective backpropagation technique, TSSL-BP or SGD-BP, for deeper spiking neural networks (SNNs) with convolution filters across various datasets; and (ii) to assess the feasibility of deploying DCSNNs trained using backpropagation techniques on low-power FPGA for inference, considering potential configuration adjustments and power requirements. The aforementioned objectives will assist in informing researchers and companies in this field regarding the limitations and unique perspectives of deploying DCSNNs on low-power FPGA devices. The study contributions have three main aspects: (i) the design of a low-power FPGA board featuring a deployable DCSNN chip suitable for object classification tasks; (ii) the inference of TSSL-BP and SGD-BP models with novel network architectures on the FPGA board for object classification tasks; and (iii) a comparative evaluation of the selected spike-based backpropagation techniques and the object classification performance of DCSNNs across multiple metrics using both public (MNIST, CIFAR10, KITTI) and private (INHA_ADAS, INHA_KLP) datasets.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

“…We tested the DCSNNs on automotive relevant datasets such as KITTI, INHA_ADAS, and INHA_KLP as opposed to just MNIST and CIFAR10, as was the case in [ 49 , 52 , 54 , 55 , 56 ].…”

Section: Literature Reviewmentioning

confidence: 99%

See 3 more Smart Citations