Implementing Spiking Neural Networks on Neuromorphic Architectures: A Review

Huynh, Phu Khanh; Varshika, M. Lakshmi; Paul, Ankan; Işık, Murat; Balaji, Adarsha; Das, Anup

doi:10.48550/arxiv.2202.08897

Cited by 10 publications

(8 citation statements)

References 210 publications

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“…While traditional von Neumann architectures have one or more central processing units physically separated from the main memory, neuromorphic architectures exploit the co-localization of memory and compute, near and in-memory computation [18]. Simultaneously to the tremendous progress in devising novel neuromorphic computing architectures, there has been many recent works that address how to map and compile (trained) SNNs models for efficient execution in neuromorphic hardware [19][20][21][22][23][24][25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Nonvolatile Memories in Spiking Neural Network Architectures: Current and Emerging Trends

2022

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A sustainable computing scenario demands more energy-efficient processors. Neuromorphic systems mimic biological functions by employing spiking neural networks for achieving brain-like efficiency, speed, adaptability, and intelligence. Current trends in neuromorphic technologies address the challenges of investigating novel materials, systems, and architectures for enabling high-integration and extreme low-power brain-inspired computing. This review collects the most recent trends in exploiting the physical properties of nonvolatile memory technologies for implementing efficient in-memory and in-device computing with spike-based neuromorphic architectures.

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Section: Introductionmentioning

confidence: 99%

Nonvolatile Memories in Spiking Neural Network Architectures: Current and Emerging Trends

2022

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“…Considering the topology implementation and the updating speed of network parameters, the most commonly used feedforward and recurrent spiking networks have gradually become the basic components of complex structures. [130][131][132] The neurons are arranged in layers in the feed-forward neural network, and each neuron is combined in a fully connected form. The spiking sequence of the input layer represents the specific task encoding, the hidden layer's spiking sequence represents the information transmission, and the output layer's spiking sequence represents the network task results.…”

Section: Spiking Neural Network Topologymentioning

confidence: 99%

Integrated Memristor Network for Physiological Signal Processing

Cai

Yue

et al. 2023

Adv Elect Materials

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Humans are complex organisms made by millions of physiological systems. Therefore, physiological activities can represent physical or mental states of the human body. Physiological signal processing is essential in monitoring human physiological features. For example, non‐invasive electroencephalography (EEG) signals can be used to reconstruct brain consciousness and detect eye movements for identity verification. However, physiological signal processing requires high resolution, high sensitivity, fast responses, and low power consumption, hindering practical hardware design for physiological signal processing. The bionic capability of memristor devices is very promising in the context of building physiological signal processing hardware and they have demonstrated a handful of advantages over the traditional Von Neumann architecture system in accelerating neural networks. Memristor networks can be integrated as a hardware system for physiological signal processing that can deliver higher energy efficiency and lower latency compared to traditional implementations. This review paper first introduces memristor characteristics, followed by a comprehensive literature study of memristor‐based networks. Physiology signal processing applications enabled by these integrated memristor networks are also presented in this review. In summary, this paper aims to provide a new perspective on physiological signal processing using integrated memristor networks.

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“…Considerable effort has been devoted to the hardware implementation of spiking neural networks, with a particular focus on FPGA-based and Application-Specific Integrated Circuit (ASIC) systems [7,8]. The paper presented in [9] describes the implementation of a spiking neural network model on a Xilinx FPGA evaluation board, utilizing a hybrid updating algorithm that combines conventional time-stepped updating and event-driven updating techniques, while using 16-bit signed fixed-point number representation.…”

Section: Related Workmentioning

confidence: 99%

Complex-Exponential-Based Bio-Inspired Neuron Model Implementation in FPGA Using Xilinx System Generator and Vivado Design Suite

Ahmad,

Zhang,

et al. 2023

Biomimetics

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This research investigates the implementation of complex-exponential-based neurons in FPGA, which can pave the way for implementing bio-inspired spiking neural networks to compensate for the existing computational constraints in conventional artificial neural networks. The increasing use of extensive neural networks and the complexity of models in handling big data lead to higher power consumption and delays. Hence, finding solutions to reduce computational complexity is crucial for addressing power consumption challenges. The complex exponential form effectively encodes oscillating features like frequency, amplitude, and phase shift, streamlining the demanding calculations typical of conventional artificial neurons through levering the simple phase addition of complex exponential functions. The article implements such a two-neuron and a multi-neuron neural model using the Xilinx System Generator and Vivado Design Suite, employing 8-bit, 16-bit, and 32-bit fixed-point data format representations. The study evaluates the accuracy of the proposed neuron model across different FPGA implementations while also providing a detailed analysis of operating frequency, power consumption, and resource usage for the hardware implementations. BRAM-based Vivado designs outperformed Simulink regarding speed, power, and resource efficiency. Specifically, the Vivado BRAM-based approach supported up to 128 neurons, showcasing optimal LUT and FF resource utilization. Such outcomes accommodate choosing the optimal design procedure for implementing spiking neural networks on FPGAs.

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Implementing Spiking Neural Networks on Neuromorphic Architectures: A Review

Cited by 10 publications

References 210 publications

Nonvolatile Memories in Spiking Neural Network Architectures: Current and Emerging Trends

Nonvolatile Memories in Spiking Neural Network Architectures: Current and Emerging Trends

Integrated Memristor Network for Physiological Signal Processing

Complex-Exponential-Based Bio-Inspired Neuron Model Implementation in FPGA Using Xilinx System Generator and Vivado Design Suite

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