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Lim JG, Park SJ, Lee SM, Jeong Y, Kim J, Lee S, Park J, Hwang GW, Lee KS, Park S, Jang HJ, Ju BK, Park JK, Kim I. Hybrid CMOS-Memristor synapse circuits for implementing Ca ion-based plasticity model. Sci Rep 2024; 14:17915. [PMID: 39095461 PMCID: PMC11297293 DOI: 10.1038/s41598-024-68359-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Accepted: 07/23/2024] [Indexed: 08/04/2024] Open
Abstract
Neuromorphic computing research is being actively pursued to address the challenges posed by the need for energy-efficient processing of big data. One of the promising approaches to tackle the challenges is the hardware implementation of spiking neural networks (SNNs) with bio-plausible learning rules. Numerous research works have been done to implement the SNN hardware with different synaptic plasticity rules to emulate human brain operations. While a standard spike-timing-dependent-plasticity (STDP) rule is emulated in many SNN hardware, the various STDP rules found in the biological brain have rarely been implemented in hardware. This study proposes a CMOS-memristor hybrid synapse circuit for the hardware implementation of a Ca ion-based plasticity model to emulate the various STDP curves. The memristor was adopted as a memory device in the CMOS synapse circuit because memristors have been identified as promising candidates for analog non-volatile memory devices in terms of energy efficiency and scalability. The circuit design was divided into four sub-blocks based on biological behavior, exploiting the non-volatile and analog state properties of memristors. The circuit was designed to vary weights using an H-bridge circuit structure and PWM modulation. The various STDP curves have been emulated in one CMOS-memristor hybrid circuit, and furthermore a simple neural network operation was demonstrated for associative learning such as Pavlovian conditioning. The proposed circuit is expected to facilitate large-scale operations for neuromorphic computing through its scale-up.
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Affiliation(s)
- Jae Gwang Lim
- Center for Semiconductor Technology, Korea Institute of Science and Technology, Seoul, 02792, South Korea
- School of Electrical Engineering, Korea University, Seoul, 02841, South Korea
| | - Sung-Jae Park
- Center for Semiconductor Technology, Korea Institute of Science and Technology, Seoul, 02792, South Korea
- Department of Micro/Nano Systems, Korea University, Seoul, 02841, South Korea
| | - Sang Min Lee
- Center for Semiconductor Technology, Korea Institute of Science and Technology, Seoul, 02792, South Korea
- Department of Micro/Nano Systems, Korea University, Seoul, 02841, South Korea
| | - Yeonjoo Jeong
- Center for Semiconductor Technology, Korea Institute of Science and Technology, Seoul, 02792, South Korea
| | - Jaewook Kim
- Center for Semiconductor Technology, Korea Institute of Science and Technology, Seoul, 02792, South Korea
| | - Suyoun Lee
- Center for Semiconductor Technology, Korea Institute of Science and Technology, Seoul, 02792, South Korea
| | - Jongkil Park
- Center for Semiconductor Technology, Korea Institute of Science and Technology, Seoul, 02792, South Korea
| | - Gyu Weon Hwang
- Center for Semiconductor Technology, Korea Institute of Science and Technology, Seoul, 02792, South Korea
| | - Kyeong-Seok Lee
- Center for Semiconductor Technology, Korea Institute of Science and Technology, Seoul, 02792, South Korea
| | - Seongsik Park
- Center for Semiconductor Technology, Korea Institute of Science and Technology, Seoul, 02792, South Korea
| | - Hyun Jae Jang
- Center for Semiconductor Technology, Korea Institute of Science and Technology, Seoul, 02792, South Korea
| | - Byeong-Kwon Ju
- School of Electrical Engineering, Korea University, Seoul, 02841, South Korea
- Department of Micro/Nano Systems, Korea University, Seoul, 02841, South Korea
| | - Jong Keuk Park
- Center for Semiconductor Technology, Korea Institute of Science and Technology, Seoul, 02792, South Korea.
| | - Inho Kim
- Center for Semiconductor Technology, Korea Institute of Science and Technology, Seoul, 02792, South Korea.
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Bahrami MK, Nazari S. Digital design of a spatial-pow-STDP learning block with high accuracy utilizing pow CORDIC for large-scale image classifier spatiotemporal SNN. Sci Rep 2024; 14:3388. [PMID: 38337032 PMCID: PMC10858263 DOI: 10.1038/s41598-024-54043-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 02/07/2024] [Indexed: 02/12/2024] Open
Abstract
The paramount concern of highly accurate energy-efficient computing in machines with significant cognitive capabilities aims to enhance the accuracy and efficiency of bio-inspired Spiking Neural Networks (SNNs). This paper addresses this main objective by introducing a novel spatial power spike-timing-dependent plasticity (Spatial-Pow-STDP) learning rule as a digital block with high accuracy in a bio-inspired SNN model. Motivated by the demand for precise and accelerated computation that reduces high-cost resources in neural network applications, this paper presents a methodology based on COordinate Rotation DIgital Computer (CORDIC) definitions. The proposed designs of CORDIC algorithms for exponential (Exp CORDIC), natural logarithm (Ln CORDIC), and arbitrary power function (Pow CORDIC) are meticulously detailed and evaluated to ensure optimal acceleration and accuracy, which respectively show average errors near 10-9, 10-6, and 10-5 with 4, 4, and 6 iterations. The engineered architectures for the Exp, Ln, and Pow CORDIC implementations are illustrated and assessed, showcasing the efficiency achieved through high frequency, leading to the introduction of a Spatial-Pow-STDP learning block design based on Pow CORDIC that facilitates efficient and accurate hardware computation with 6.93 × 10-3 average error with 9 iterations. The proposed learning mechanism integrates this structure into a large-scale spatiotemporal SNN consisting of three layers with reduced hyper-parameters, enabling unsupervised training in an event-based paradigm using excitatory and inhibitory synapses. As a result, the application of the developed methodology and equations in the computational SNN model for image classification reveals superior accuracy and convergence speed compared to existing spiking networks by achieving up to 97.5%, 97.6%, 93.4%, and 93% accuracy, respectively, when trained on the MNIST, EMNIST digits, EMNIST letters, and CIFAR10 datasets with 6, 2, 2, and 6 training epochs.
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Affiliation(s)
| | - Soheila Nazari
- Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, 1983969411, Iran.
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Pham MD, D’Angiulli A, Dehnavi MM, Chhabra R. From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems? Brain Sci 2023; 13:1316. [PMID: 37759917 PMCID: PMC10526461 DOI: 10.3390/brainsci13091316] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 09/05/2023] [Accepted: 09/07/2023] [Indexed: 09/29/2023] Open
Abstract
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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Affiliation(s)
- Martin Do Pham
- Department of Computer Science, University of Toronto, Toronto, ON M5S 1A1, Canada; (M.D.P.); (M.M.D.)
| | - Amedeo D’Angiulli
- Department of Neuroscience, Carleton University, Ottawa, ON K1S 5B6, Canada;
| | - Maryam Mehri Dehnavi
- Department of Computer Science, University of Toronto, Toronto, ON M5S 1A1, Canada; (M.D.P.); (M.M.D.)
| | - Robin Chhabra
- Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada
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Oh S, An J, Cho S, Yoon R, Min KS. Memristor Crossbar Circuits Implementing Equilibrium Propagation for On-Device Learning. MICROMACHINES 2023; 14:1367. [PMID: 37512678 PMCID: PMC10384638 DOI: 10.3390/mi14071367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 05/22/2023] [Accepted: 07/01/2023] [Indexed: 07/30/2023]
Abstract
Equilibrium propagation (EP) has been proposed recently as a new neural network training algorithm based on a local learning concept, where only local information is used to calculate the weight update of the neural network. Despite the advantages of local learning, numerical iteration for solving the EP dynamic equations makes the EP algorithm less practical for realizing edge intelligence hardware. Some analog circuits have been suggested to solve the EP dynamic equations physically, not numerically, using the original EP algorithm. However, there are still a few problems in terms of circuit implementation: for example, the need for storing the free-phase solution and the lack of essential peripheral circuits for calculating and updating synaptic weights. Therefore, in this paper, a new analog circuit technique is proposed to realize the EP algorithm in practical and implementable hardware. This work has two major contributions in achieving this objective. First, the free-phase and nudge-phase solutions are calculated by the proposed analog circuits simultaneously, not at different times. With this process, analog voltage memories or digital memories with converting circuits between digital and analog domains for storing the free-phase solution temporarily can be eliminated in the proposed EP circuit. Second, a simple EP learning rule relying on a fixed amount of conductance change per programming pulse is newly proposed and implemented in peripheral circuits. The modified EP learning rule can make the weight update circuit practical and implementable without requiring the use of a complicated program verification scheme. The proposed memristor conductance update circuit is simulated and verified for training synaptic weights on memristor crossbars. The simulation results showed that the proposed EP circuit could be used for realizing on-device learning in edge intelligence hardware.
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Affiliation(s)
- Seokjin Oh
- School of Electrical Engineering, Kookmin University, Seoul 02707, Republic of Korea
| | - Jiyong An
- School of Electrical Engineering, Kookmin University, Seoul 02707, Republic of Korea
| | - Seungmyeong Cho
- School of Electrical Engineering, Kookmin University, Seoul 02707, Republic of Korea
| | - Rina Yoon
- School of Electrical Engineering, Kookmin University, Seoul 02707, Republic of Korea
| | - Kyeong-Sik Min
- School of Electrical Engineering, Kookmin University, Seoul 02707, Republic of Korea
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Guerrero-Rodriguez JM, Cifredo-Chacon MA, Cobos Sánchez C, Perez-Peña F. Exploiting the PIR Sensor Analog Behavior as Thermoreceptor: Movement Direction Classification Based on Spiking Neurons. SENSORS (BASEL, SWITZERLAND) 2023; 23:5816. [PMID: 37447667 DOI: 10.3390/s23135816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 06/19/2023] [Accepted: 06/20/2023] [Indexed: 07/15/2023]
Abstract
Pyroelectric infrared sensors (PIR) are widely used as infrared (IR) detectors due to their basic implementation, low cost, low power, and performance. Combined with a Fresnel lens, they can be used as a binary detector in applications of presence and motion control. Furthermore, due to their features, they can be used in autonomous intelligent devices or included in robotics applications or sensor networks. In this work, two neural processing architectures are presented: (1) an analog processing approach to achieve the behavior of a presynaptic neuron from a PIR sensor. An analog circuit similar to the leaky integrate and fire model is implemented to be able to generate spiking rates proportional to the IR stimuli received at a PIR sensor. (2) An embedded postsynaptic neuron where a spiking neural network matrix together with an algorithm based on digital processing techniques is introduced. This structure allows connecting a set of sensors to the post-synaptic circuit emulating an optic nerve. As a case study, the entire neural processing approach presented in this paper is applied to optical flow detection considering a four-PIR array as input. The results validate both the spiking approach for an analog sensor presented and the ability to retrieve the analog information sent as spike trains in a simulated optic nerve.
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Affiliation(s)
- Jose-Maria Guerrero-Rodriguez
- Microelectronic Circuit Design Group, Engineering School, University of Cadiz, Campus Universitario de Puerto Real, Avda. Universidad de Cádiz, nº 10, CP 11519 Puerto Real, Cádiz, Spain
| | - Maria-Angeles Cifredo-Chacon
- Microelectronic Circuit Design Group, Engineering School, University of Cadiz, Campus Universitario de Puerto Real, Avda. Universidad de Cádiz, nº 10, CP 11519 Puerto Real, Cádiz, Spain
| | - Clemente Cobos Sánchez
- Microelectronic Circuit Design Group, Engineering School, University of Cadiz, Campus Universitario de Puerto Real, Avda. Universidad de Cádiz, nº 10, CP 11519 Puerto Real, Cádiz, Spain
| | - Fernando Perez-Peña
- Applied Robotics Lab, Engineering School, University of Cadiz, Campus Universitario de Puerto Real, Avda. Universidad de Cádiz, nº 10, CP 11519 Puerto Real, Cádiz, Spain
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Wang H, Pan X, Wang J, Sun M, Wu C, Yu Q, Liu Z, Chen T, Liu Y. Dual functional states of working memory realized by memristor-based neural network. Front Neurosci 2023; 17:1192993. [PMID: 37351423 PMCID: PMC10282152 DOI: 10.3389/fnins.2023.1192993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 05/16/2023] [Indexed: 06/24/2023] Open
Abstract
Working memory refers to the brain's ability to store and manipulate information for a short period. It is disputably considered to rely on two mechanisms: sustained neuronal firing, and "activity-silent" working memory. To develop a highly biologically plausible neuromorphic computing system, it is anticipated to physically realize working memory that corresponds to both of these mechanisms. In this study, we propose a memristor-based neural network to realize the sustained neural firing and activity-silent working memory, which are reflected as dual functional states within memory. Memristor-based synapses and two types of artificial neurons are designed for the Winner-Takes-All learning rule. During the cognitive task, state transformation between the "focused" state and the "unfocused" state of working memory is demonstrated. This work paves the way for further emulating the complex working memory functions with distinct neural activities in our brains.
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Affiliation(s)
- Hongzhe Wang
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, China
| | - Xinqiang Pan
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, China
| | - Junjie Wang
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, China
| | - Mingyuan Sun
- Beijing China Changfeng Electromechanical Technology Research and Design Institute, Beijing, China
| | - Chuangui Wu
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, China
| | - Qi Yu
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhen Liu
- School of Materials and Energy, Guangdong University of Technology, Guangzhou, China
| | - Tupei Chen
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
| | - Yang Liu
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, China
- Deepcreatic Technologies Ltd., Chengdu, China
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Shi DQ, Xu F, Bi GQ, Lau PM. Conserved Spatiotemporal Dynamics with Millisecond Precision During Reverberatory Activity in a Self-organized Neuronal Network. Neurosci Bull 2023; 39:808-812. [PMID: 36881371 PMCID: PMC10170005 DOI: 10.1007/s12264-023-01033-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Accepted: 09/27/2022] [Indexed: 03/08/2023] Open
Affiliation(s)
- Dong-Qing Shi
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China
| | - Fang Xu
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China
- Interdisciplinary Center for Brain Information, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Guo-Qiang Bi
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China.
- Interdisciplinary Center for Brain Information, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
| | - Pak-Ming Lau
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China.
- Interdisciplinary Center for Brain Information, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
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Liu F, Tao W, Yang J, Wu W, Wang J. STNet: A novel spiking neural network combining its own time signal with the spatial signal of an artificial neural network. Front Neurosci 2023; 17:1151949. [PMID: 37144088 PMCID: PMC10153670 DOI: 10.3389/fnins.2023.1151949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 03/10/2023] [Indexed: 05/06/2023] Open
Abstract
Introduction This article proposes a novel hybrid network that combines the temporal signal of a spiking neural network (SNN) with the spatial signal of an artificial neural network (ANN), namely the Spatio-Temporal Combined Network (STNet). Methods Inspired by the way the visual cortex in the human brain processes visual information, two versions of STNet are designed: a concatenated one (C-STNet) and a parallel one (P-STNet). In the C-STNet, the ANN, simulating the primary visual cortex, extracts the simple spatial information of objects first, and then the obtained spatial information is encoded as spiking time signals for transmission to the rear SNN which simulates the extrastriate visual cortex to process and classify the spikes. With the view that information from the primary visual cortex reaches the extrastriate visual cortex via ventral and dorsal streams, in P-STNet, the parallel combination of the ANN and the SNN is employed to extract the original spatio-temporal information from samples, and the extracted information is transferred to a posterior SNN for classification. Results The experimental results of the two STNets obtained on six small and two large benchmark datasets were compared with eight commonly used approaches, demonstrating that the two STNets can achieve improved performance in terms of accuracy, generalization, stability, and convergence. Discussion These prove that the idea of combining ANN and SNN is feasible and can greatly improve the performance of SNN.
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Affiliation(s)
- Fang Liu
- School of Mathematical Sciences, Dalian University of Technology, Dalian, China
- Key Laboratory for Computational Mathematics and Data Intelligence of Liaoning Province, Dalian, China
| | - Wentao Tao
- School of Mathematical Sciences, Dalian University of Technology, Dalian, China
- Key Laboratory for Computational Mathematics and Data Intelligence of Liaoning Province, Dalian, China
| | - Jie Yang
- School of Mathematical Sciences, Dalian University of Technology, Dalian, China
- Key Laboratory for Computational Mathematics and Data Intelligence of Liaoning Province, Dalian, China
- *Correspondence: Jie Yang
| | - Wei Wu
- School of Mathematical Sciences, Dalian University of Technology, Dalian, China
- Key Laboratory for Computational Mathematics and Data Intelligence of Liaoning Province, Dalian, China
| | - Jian Wang
- College of Science, China University of Petroleum (East China), Qingdao, China
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Velusamy G, Lent R. Delay-Packet-Loss-Optimized Distributed Routing Using Spiking Neural Network in Delay-Tolerant Networking. SENSORS (BASEL, SWITZERLAND) 2022; 23:310. [PMID: 36616907 PMCID: PMC9824247 DOI: 10.3390/s23010310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 12/18/2022] [Accepted: 12/21/2022] [Indexed: 06/17/2023]
Abstract
Satellite communication is inevitable due to the Internet of Everything and the exponential increase in the usage of smart devices. Satellites have been used in many applications to make human life safe, secure, sophisticated, and more productive. The applications that benefit from satellite communication are Earth observation (EO), military missions, disaster management, and 5G/6G integration, to name a few. These applications rely on the timely and accurate delivery of space data to ground stations. However, the channels between satellites and ground stations suffer attenuation caused by uncertain weather conditions and long delays due to line-of-sight constraints, congestion, and physical distance. Though inter-satellite links (ISLs) and inter-orbital links (IOLs) create multiple paths between satellite nodes, both ISLs and IOLs have the same issues. Some essential applications, such as EO, depend on time-sensitive and error-free data delivery, which needs better throughput connections. It is challenging to route space data to ground stations with better QoS by leveraging the ISLs and IOLs. Routing approaches that use the shortest path to optimize latency may cause packet losses and reduced throughput based on the channel conditions, while routing methods that try to avoid packet losses may end up delivering data with long delays. Existing routing algorithms that use multi-optimization goals tend to use priority-based optimization to optimize either of the metrics. However, critical satellite missions that depend on high-throughput and low-latency data delivery need routing approaches that optimize both metrics concurrently. We used a modified version of Kleinrock's power metric to reduce delay and packet losses and verified it with experimental evaluations. We used a cognitive space routing approach, which uses a reinforcement-learning-based spiking neural network to implement routing strategies in NASA's High Rate Delay Tolerant Networking (HDTN) project.
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Affiliation(s)
| | - Ricardo Lent
- Correspondence: (G.V.); (R.L.); Tel.: +1-713-743-4239 (R.L.)
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Arsalan M, Santra A, Issakov V. Power-efficient gesture sensing for edge devices: mimicking fourier transforms with spiking neural networks. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04258-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
AbstractOne of the key design requirements for any portable/mobile device is low power. To enable such a low powered device, we propose an embedded gesture detection system that uses spiking neural networks (SNNs) applied directly to raw ADC data of a 60GHz frequency modulated continuous wave radar. SNNs can facilitate low power systems because they are sparse in time and space and are event-driven. The proposed system, as opposed to earlier state-of-the-art methods, relies solely on the target’s raw ADC data, thus avoiding the overhead of performing slow-time and fast-time Fourier transforms (FFTs) processing. The proposed architecture mimics the discrete Fourier transformation within the SNN itself avoiding the need for FFT accelerators and makes the FFT processing tailored to the specific application, in this case gesture sensing. The experimental results demonstrate that the proposed system is capable of classifying 8 different gestures with an accuracy of 98.7%. This result is comparable to the conventional approaches, yet it offers lower complexity, lower power consumption and faster computations comparable to the conventional approaches.
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