SpikeSim: An End-to-End Compute-in-Memory Hardware Evaluation Tool for Benchmarking Spiking Neural Networks

Moitra, Abhishek; Bhattacharjee, Abhiroop; Runcong, Kuang,; Krishnan, Gokul; Cao, Yu; Panda, Priyadarshini

doi:10.1109/tcad.2023.3274918

Cited by 12 publications

(3 citation statements)

References 41 publications

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“…It can be seen that SNNs consume up to 94% less energy than ANNs, which could largely promote the battery life in smart devices. However, in the image processing domain, SNNs may have higher data-moving energy because they need to store the membrane potential and access them in the future (Yin et al, 2022 , 2023 ; Moitra et al, 2023 ). We demonstrate that, in the HAR domain, SNNs have even lower data-moving energy than ANNs.…”

Section: Methodsmentioning

confidence: 99%

Efficient human activity recognition with spatio-temporal spiking neural networks

Li,

Yin,

Kim

et al. 2023

Front. Neurosci.

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In this study, we explore Human Activity Recognition (HAR), a task that aims to predict individuals' daily activities utilizing time series data obtained from wearable sensors for health-related applications. Although recent research has predominantly employed end-to-end Artificial Neural Networks (ANNs) for feature extraction and classification in HAR, these approaches impose a substantial computational load on wearable devices and exhibit limitations in temporal feature extraction due to their activation functions. To address these challenges, we propose the application of Spiking Neural Networks (SNNs), an architecture inspired by the characteristics of biological neurons, to HAR tasks. SNNs accumulate input activation as presynaptic potential charges and generate a binary spike upon surpassing a predetermined threshold. This unique property facilitates spatio-temporal feature extraction and confers the advantage of low-power computation attributable to binary spikes. We conduct rigorous experiments on three distinct HAR datasets using SNNs, demonstrating that our approach attains competitive or superior performance relative to ANNs, while concurrently reducing energy consumption by up to 94%.

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Section: Methodsmentioning

confidence: 99%

Efficient human activity recognition with spatio-temporal spiking neural networks

Li,

Yin,

Kim

et al. 2023

Front. Neurosci.

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“…With efficient storage of synaptic weights and specialized memory technologies, neuromorphic architectures provide scalable and brain-inspired computing solutions. Tool development to evaluate this hardware is also an active field of research, one such example being the work of [ 51 ] which introduced SpikeSim, a platform for an end-to-end compute-in-memory benchmarking tool to compare different SNN models on a chip for their power and latency efficiency.…”

Section: Neuromorphic Computing: Hardware Vs Softwarementioning

confidence: 99%

From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems?

Pham,

D’Angiulli,

Dehnavi

et al. 2023

Brain Sciences

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We examine the challenging “marriage” between computational efficiency and biological plausibility—A crucial node in the domain of spiking neural networks at the intersection of neuroscience, artificial intelligence, and robotics. Through a transdisciplinary review, we retrace the historical and most recent constraining influences that these parallel fields have exerted on descriptive analysis of the brain, construction of predictive brain models, and ultimately, the embodiment of neural networks in an enacted robotic agent. We study models of Spiking Neural Networks (SNN) as the central means enabling autonomous and intelligent behaviors in biological systems. We then provide a critical comparison of the available hardware and software to emulate SNNs for investigating biological entities and their application on artificial systems. Neuromorphics is identified as a promising tool to embody SNNs in real physical systems and different neuromorphic chips are compared. The concepts required for describing SNNs are dissected and contextualized in the new no man’s land between cognitive neuroscience and artificial intelligence. Although there are recent reviews on the application of neuromorphic computing in various modules of the guidance, navigation, and control of robotic systems, the focus of this paper is more on closing the cognition loop in SNN-embodied robotics. We argue that biologically viable spiking neuronal models used for electroencephalogram signals are excellent candidates for furthering our knowledge of the explainability of SNNs. We complete our survey by reviewing different robotic modules that can benefit from neuromorphic hardware, e.g., perception (with a focus on vision), localization, and cognition. We conclude that the tradeoff between symbolic computational power and biological plausibility of hardware can be best addressed by neuromorphics, whose presence in neurorobotics provides an accountable empirical testbench for investigating synthetic and natural embodied cognition. We argue this is where both theoretical and empirical future work should converge in multidisciplinary efforts involving neuroscience, artificial intelligence, and robotics.

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“…Digital implementations of SNNs are often favored for their compatibility with existing digital systems, and their performance can be reliably estimated based on familiar digital frameworks, making them a popular choice. [ 7 ] While digital SNN systems are considerably more energy‐efficient than conventional von Neumann architecture and offer predictability when compared to analog SNN systems, it is important to note that their reliance on synchronous processing based on an intrinsic digital architecture sets them apart from the human brain and can lead to different results in their speed and power consumption. To date, numerous studies have employed fully analog systems with analog synapses and integrate‐and‐fire (I&F) neurons to implement SNNs.…”

Section: Introductionmentioning

confidence: 99%

SNNSim: Investigation and Optimization of Large‐Scale Analog Spiking Neural Networks Based on Flash Memory Devices

Ko,

Kwon,

Hwang

et al. 2024

Advanced Intelligent Systems

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Spiking neural networks (SNNs) have emerged as a novel approach for reducing computational costs by mimicking the biologically plausible operations of neurons and synapses. In this article, large‐scale analog SNNs are investigated and optimized at the hardware‐level by using SNNSim, the novel simulator for SNNs that employ analog synaptic devices and integrate‐and‐fire (I&F) neuron circuits. SNNSim is a reconfigurable simulator that accurately and very quickly models the behavior of the user‐defined device characteristics and returns key metrics such as area, accuracy, latency, and power consumption as output. Notably, SNNSim exhibits exceptional efficiency, as it can process the entire 10 000 Modified National Institute of Standards and Technology (MNIST) test dataset in a few seconds, whereas SPICE simulations require hours to simulate a single MNIST test data. Using SNNSim, the conversion of artificial neural networks (ANNs) to SNNs is simulated and the performance of the large‐scale analog SNNs is optimized. The results enable the design of accurate, high‐speed, and low‐power operation of large‐scale SNNs. SNNSim code is now available at https://github.com/SMDLGITHUB/SNNSim.

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SpikeSim: An End-to-End Compute-in-Memory Hardware Evaluation Tool for Benchmarking Spiking Neural Networks

Cited by 12 publications

References 41 publications

Efficient human activity recognition with spatio-temporal spiking neural networks

Efficient human activity recognition with spatio-temporal spiking neural networks

From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems?

SNNSim: Investigation and Optimization of Large‐Scale Analog Spiking Neural Networks Based on Flash Memory Devices

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