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A Unified Evaluation Framework for Spiking Neural Network Hardware Accelerators Based on Emerging Non-Volatile Memory Devices
Other Condensed Matter · Submitted 29 Feb 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Utilizing our proposed skyrmionic devices, the constructed SNN demonstrates an inference accuracy of approximately 98% and achieves energy consumption on the order of pJ when processing the Modified National Institute of Standards and Technology (MNIST) handwritten digit dataset.
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Spiking Neural Networks (SNNs) have emerged as a promising paradigm, offering event-driven and energy-efficient computation. In recent studies, various devices tailored for SNN synapses and neurons have been proposed, leveraging the unique characteristics of emerging non-volatile memory (eNVM) technologies. While substantial progress has been made in exploring the capabilities of SNNs and designing dedicated hardware components, there exists a critical gap in establishing a unified approach for evaluating hardware-level metrics. Specifically, metrics such as latency, and energy consumption, are pivotal in assessing the practical viability and efficiency of the constructed neural network. In this article, we address this gap by presenting a comprehensive framework for evaluating hardware-level metrics in SNNs based on non-volatile memory devices. We systematically analyze the impact of synaptic and neuronal components on energy consumption providing a unified perspective for assessing the overall efficiency of the network. In this study, our emphasis lies on the neuron and synaptic device based on magnetic skyrmions. Nevertheless, our framework is versatile enough to encompass other emerging devices as well. Utilizing our proposed skyrmionic devices, the constructed SNN demonstrates an inference accuracy of approximately 98% and achieves energy consumption on the order of pJ when processing the Modified National Institute of Standards and Technology (MNIST) handwritten digit dataset.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Spiking Neural Networks (SNNs) have emerged as a promising paradigm, offering event-driven and energy-efficient computation. | background 1.00 | 0.04 | 0.02 | asserted 0.94 |
| s1 | In recent studies, various devices tailored for SNN synapses and neurons have been proposed, leveraging the unique characteristics of emerging non-volatile memory (eNVM) technologies. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s2 | While substantial progress has been made in exploring the capabilities of SNNs and designing dedicated hardware components, there exists a critical gap in establishing a unified approach for evaluating hardware-level metrics. | gap 1.00 | 0.12 | 0.05 | asserted 1.00 |
| s3 | Specifically, metrics such as latency, and energy consumption, are pivotal in assessing the practical viability and efficiency of the constructed neural network. | background 0.94 | 0.07 | 0.03 | asserted 1.00 |
| s4 | In this article, we address this gap by presenting a comprehensive framework for evaluating hardware-level metrics in SNNs based on non-volatile memory devices. | contribution 1.00 | 0.90 | 0.19 | asserted 1.00 |
| s5 | We systematically analyze the impact of synaptic and neuronal components on energy consumption providing a unified perspective for assessing the overall efficiency of the network. | contribution 0.75 | 0.90 | 0.15 | asserted 1.00 |
| s6 | In this study, our emphasis lies on the neuron and synaptic device based on magnetic skyrmions. | contribution 0.64 | 0.66 | 0.04 | asserted 1.00 |
| s7 | Nevertheless, our framework is versatile enough to encompass other emerging devices as well. | contribution 0.46 | 0.45 | 0.06 | asserted 0.90 |
| s8 | Utilizing our proposed skyrmionic devices, the constructed SNN demonstrates an inference accuracy of approximately 98% and achieves energy consumption on the order of pJ when processing the Modified National Institute of Standards and Technology (MNIST) handwritten digit dataset. | result 1.00 | 0.94 | 0.54 | asserted 1.00 |
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Original abstract
Spiking Neural Networks (SNNs) have emerged as a promising paradigm, offering event-driven and energy-efficient computation. In recent studies, various devices tailored for SNN synapses and neurons have been proposed, leveraging the unique characteristics of emerging non-volatile memory (eNVM) technologies. While substantial progress has been made in exploring the capabilities of SNNs and designing dedicated hardware components, there exists a critical gap in establishing a unified approach for evaluating hardware-level metrics. Specifically, metrics such as latency, and energy consumption, are pivotal in assessing the practical viability and efficiency of the constructed neural network. In this article, we address this gap by presenting a comprehensive framework for evaluating hardware-level metrics in SNNs based on non-volatile memory devices. We systematically analyze the impact of synaptic and neuronal components on energy consumption providing a unified perspective for assessing the overall efficiency of the network. In this study, our emphasis lies on the neuron and synaptic device based on magnetic skyrmions. Nevertheless, our framework is versatile enough to encompass other emerging devices as well. Utilizing our proposed skyrmionic devices, the constructed SNN demonstrates an inference accuracy of approximately 98% and achieves energy consumption on the order of pJ when processing the Modified National Institute of Standards and Technology (MNIST) handwritten digit dataset.
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Uses the method or tool of 1 paper
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The training of the SNN is executed using the Adam optimizer [ref] with a batch size of 64 over 100 epochs.
Adam: A Method for Stochastic Optimization · Spiking Neural Network simulation · 1.00
Cites as background 2 papers
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Similarly, in natural language processing [ref] , RNN [ref] and transformer architectures [ref] have propelled machine translation, text generation, and sentiment analysis to unprecedented accuracies.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding · Introduction · 0.95
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In computer vision, DNN have played a pivotal role in revolutionizing image classification [ref] , object detection [ref] , and segmentation [ref] .
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation · Introduction · 0.94
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