Flexon: A Flexible Digital Neuron for Efficient Spiking Neural Network Simulations

Lee, Dayeol; Lee, Gwangmu; Kwon, Dongup; Lee, Sunghwa; Kim, Youngsok; Kim, Jangwoo

doi:10.1109/isca.2018.00032

Cited by 23 publications

(9 citation statements)

References 57 publications

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“…In its refractory period, the neuron cannot generate any spikes. This complicated process of neuronal behavior has been modeled and imitated by an artificial neuron, i.e., leaky integrate-and-fire (LIF), quadratic integrate-and-fire (QIF), depending on the artificial neuron model, the computational complexity varies regarding membrane decay, spike accumulation, spike initiation, and refractory behavior (Lee et al, 2018 ).…”

Section: Methodsmentioning

confidence: 99%

ReplaceNet: real-time replacement of a biological neural circuit with a hardware-assisted spiking neural network

Hwang,

Kim

et al. 2023

Front. Neurosci.

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Recent developments in artificial neural networks and their learning algorithms have enabled new research directions in computer vision, language modeling, and neuroscience. Among various neural network algorithms, spiking neural networks (SNNs) are well-suited for understanding the behavior of biological neural circuits. In this work, we propose to guide the training of a sparse SNN in order to replace a sub-region of a cultured hippocampal network with limited hardware resources. To verify our approach with a realistic experimental setup, we record spikes of cultured hippocampal neurons with a microelectrode array (in vitro). The main focus of this work is to dynamically cut unimportant synapses during SNN training on the fly so that the model can be realized on resource-constrained hardware, e.g., implantable devices. To do so, we adopt a simple STDP learning rule to easily select important synapses that impact the quality of spike timing learning. By combining the STDP rule with online supervised learning, we can precisely predict the spike pattern of the cultured network in real-time. The reduction in the model complexity, i.e., the reduced number of connections, significantly reduces the required hardware resources, which is crucial in developing an implantable chip for the treatment of neurological disorders. In addition to the new learning algorithm, we prototype a sparse SNN hardware on a small FPGA with pipelined execution and parallel computing to verify the possibility of real-time replacement. As a result, we can replace a sub-region of the biological neural circuit within 22 μs using 2.5 × fewer hardware resources, i.e., by allowing 80% sparsity in the SNN model, compared to the fully-connected SNN model. With energy-efficient algorithms and hardware, this work presents an essential step toward real-time neuroprosthetic computation.

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Section: Methodsmentioning

confidence: 99%

ReplaceNet: real-time replacement of a biological neural circuit with a hardware-assisted spiking neural network

Hwang,

Kim

et al. 2023

Front. Neurosci.

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“…Unlike most computational neuroscience frameworks such as Nest (Gewaltig and Diesmann, 2007 ) and Neuron (Carnevale and Hines, 2006 ), where neurons cannot be directly trained using gradient-based learning methods, BIDL introduces a group of neurons called LIF+ that can be trained directly using backpropagation through time (BPTT) with surrogate gradients. The LIF+ neurons are derived from the original leaky integrate and fire (LIF) model by incorporating improvements in key aspects of the differential equations to enhance neurodynamics, as inspired by Lee et al ( 2018 ). Figure 5 illustrates the decomposition of LIF+ into five stages, each offering several choices for improving the similarity to biological neural dynamics.…”

Section: Bidl: An Easy-to-use Platform For Snn Researchersmentioning

confidence: 99%

BIDL: a brain-inspired deep learning framework for spatiotemporal processing

Wu¹,

Shen²,

Zhang³

et al. 2023

Front. Neurosci.

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Brain-inspired deep spiking neural network (DSNN) which emulates the function of the biological brain provides an effective approach for event-stream spatiotemporal perception (STP), especially for dynamic vision sensor (DVS) signals. However, there is a lack of generalized learning frameworks that can handle various spatiotemporal modalities beyond event-stream, such as video clips and 3D imaging data. To provide a unified design flow for generalized spatiotemporal processing (STP) and to investigate the capability of lightweight STP processing via brain-inspired neural dynamics, this study introduces a training platform called brain-inspired deep learning (BIDL). This framework constructs deep neural networks, which leverage neural dynamics for processing temporal information and ensures high-accuracy spatial processing via artificial neural network layers. We conducted experiments involving various types of data, including video information processing, DVS information processing, 3D medical imaging classification, and natural language processing. These experiments demonstrate the efficiency of the proposed method. Moreover, as a research framework for researchers in the fields of neuroscience and machine learning, BIDL facilitates the exploration of different neural models and enables global-local co-learning. For easily fitting to neuromorphic chips and GPUs, the framework incorporates several optimizations, including iteration representation, state-aware computational graph, and built-in neural functions. This study presents a user-friendly and efficient DSNN builder for lightweight STP applications and has the potential to drive future advancements in bio-inspired research.

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“…Some neuron models such as the QIF model (Benjamin et al, 2014) and adaptive exponential integrate and fire model (AdEx model) (Brette and Gerstner, 2005) do not instantly produce a spike. These neuron models hire alternative non-instant functions, which control the membrane potential once it reaches the threshold voltage (Lee et al, 2018).…”

Section: Data Availability Statementmentioning

confidence: 99%

A Digital Hardware System for Spiking Network of Tactile Afferents

et al. 2020

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In the present research, we explore the possibility of utilizing a hardware-based neuromorphic approach to develop a tactile sensory system at the level of first-order afferents, which are slowly adapting type 1 (SA-I) and fast adapting type 1 (FA-I) afferents. Four spiking models are used to mimic neural signals of both SA-I and FA-I primary afferents. Next, a digital circuit is designed for each spiking model for both afferents to be implemented on the field-programmable gate array (FPGA). The four different digital circuits are then compared from source utilization point of view to find the minimum cost circuit for creating a population of digital afferents. In this way, the firing responses of both SA-I and FA-I afferents are physically measured in hardware. Finally, a population of 243 afferents consisting of 90 SA-I and 153 FA-I digital neuromorphic circuits are implemented on the FPGA. The FPGA also receives nine inputs from the force sensors through an interfacing board. Therefore, the data of multiple inputs are processed by the spiking network of tactile afferents, simultaneously. Benefiting from parallel processing capabilities of FPGA, the proposed architecture offers a low-cost neuromorphic structure for tactile information processing. Applying machine learning algorithms on the artificial spiking patterns collected from FPGA, we successfully classified three different objects based on the firing rate paradigm. Consequently, the proposed neuromorphic system provides the opportunity for development of new tactile processing component for robotic and prosthetic applications.

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Flexon: A Flexible Digital Neuron for Efficient Spiking Neural Network Simulations

Cited by 23 publications

References 57 publications

ReplaceNet: real-time replacement of a biological neural circuit with a hardware-assisted spiking neural network

ReplaceNet: real-time replacement of a biological neural circuit with a hardware-assisted spiking neural network

BIDL: a brain-inspired deep learning framework for spatiotemporal processing

A Digital Hardware System for Spiking Network of Tactile Afferents

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