A Fully Spiking Hybrid Neural Network for Energy-Efficient Object Detection

Chakraborty, Biswadeep; She, Xueyuan; Mukhopadhyay, Saibal

doi:10.1109/tip.2021.3122092

Cited by 27 publications

(10 citation statements)

References 33 publications

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“…Hybrid networks with supervised training can help us improve the accuracy of the STDP network. Thus, we simulate the hybrid supervised-unsupervised learning methodology similar to the works done by Chakraborty et al ( 2021 ).…”

Section: Simulation Resultsmentioning

confidence: 99%

“…As we discussed in section 2.4, hybrid networks can help us improve the accuracy of the STDP network. Thus, in this article, we used a hybrid supervised-unsupervised learning methodology similar to the works done by Chakraborty et al ( 2021 ). Supervised learning is the surrogate gradient-based training of the SNNs (Wu et al, 2018 ; Neftci et al, 2019 ).…”

Section: Hardware Architecturementioning

confidence: 99%

“…Previously, the concept of hybridization combining supervised training and STDP has been first introduced for a DNN (She et al, 2021 ). After that, Chakraborty et al has shown the same concept of hybridization on SNN (Chakraborty et al, 2021 ). In this article, we show the hardware platform to accelerate the ConvSNN using the same concept of hybridization.…”

Section: Introductionmentioning

confidence: 96%

“…Hence, following the work done by Chakraborty et al ( 2021 ), we use a hybrid network consisting of surrogate-gradient based backpropagated ConvSNN modules along with the unsupervised STDP trained ConvSNN module. Figure 6 shows the architectural block diagrams of the different types of neural networks.…”

Section: Introductionmentioning

confidence: 99%

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MONETA: A Processing-In-Memory-Based Hardware Platform for the Hybrid Convolutional Spiking Neural Network With Online Learning

et al. 2022

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We present a processing-in-memory (PIM)-based hardware platform, referred to as MONETA, for on-chip acceleration of inference and learning in hybrid convolutional spiking neural network. MONETAuses 8T static random-access memory (SRAM)-based PIM cores for vector matrix multiplication (VMM) augmented with spike-time-dependent-plasticity (STDP) based weight update. The spiking neural network (SNN)-focused data flow is presented to minimize data movement in MONETAwhile ensuring learning accuracy. MONETAsupports on-line and on-chip training on PIM architecture. The STDP-trained convolutional neural network within SNN (ConvSNN) with the proposed data flow, 4-bit input precision, and 8-bit weight precision shows only 1.63% lower accuracy in CIFAR-10 compared to the STDP accuracy implemented by the software. Further, the proposed architecture is used to accelerate a hybrid SNN architecture that couples off-chip supervised (back propagation through time) and on-chip unsupervised (STDP) training. We also evaluate the hybrid network architecture with the proposed data flow. The accuracy of this hybrid network is 10.84% higher than STDP trained accuracy result and 1.4% higher compared to the backpropagated training-based ConvSNN result with the CIFAR-10 dataset. Physical design of MONETAin 65 nm complementary metal-oxide-semiconductor (CMOS) shows 18.69 tera operation per second (TOPS)/W, 7.25 TOPS/W and 10.41 TOPS/W power efficiencies for the inference mode, learning mode, and hybrid learning mode, respectively.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Hardware Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

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MONETA: A Processing-In-Memory-Based Hardware Platform for the Hybrid Convolutional Spiking Neural Network With Online Learning

et al. 2022

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“…We calculate the energy consumption of the SNN by multiplying floating-point operations (FLOPS) and the energy consumption of MAC and AC operations. We use the same energy-efficiency calculations as in Chakraborty et al., 62 and the computation details can be seen in Equation 1 .

…”

Section: Discussionmentioning

confidence: 99%