An improved spiking network conversion for image classification

Nguyen, Thu Quyen; Phạm, Quốc Trung; Hoang, Phuong; Dang, Quang Hieu; Nguyen, Duc Minh; Nguyễn, Huy Hoàng

doi:10.1109/mapr53640.2021.9585199

Cited by 8 publications

(3 citation statements)

References 19 publications

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“…Notably, Loihi stands out as the most energyefficient among other neuromorphic chips. This trend in the energy ratio between CPUs, GPUs, and Loihi is consistent across various networks [43]. To observe the impact of high amplitude vibrations on energy consumption, the change in energy consumption with increase in flow rate has been plotted for GPU and Loihi for PS2 sensor and 2mm leak state in Figure 23.…”

Section: Comparison Of the Approximate Energy Consumption Across Hard...supporting

confidence: 52%

Detection of Pipeline Leaks using BiopiezoelectricEnergy Harvesting Sensors via RecurrenceQuantification and Neuromorphic Computing

Rounak,

Banerjee,

Joseph

et al. 2024

Preprint

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This paper presents a study on water pipeline leak detection using organic glycineenergy harvesting sensors. The analysis involves examining open circuit voltageswith recurrence quantification analysis (RQA), complexity measures, and neuromorphiccomputing techniques. Time-dependent un-thresholded recurrence plotsreveal nonlinear characteristics in bio-piezoelectric sensed signatures, providinginsights into leak intensities, flow rates, and sensor locations over short and longtime scales. RQA measures successfully distinguish between leak states, withincreased flow rates resulting in notable changes. The results indicate that glycinesensors outperform PVDF sensors in accurate leak detection. The methodologyis adapted to a neuromorphic framework, estimating leak occurrence and sizeclassification. The spiking neural network-based model shows the superiority ofbio-piezoelectric sensors for structural health monitoring within a neuromorphicframework, compared to PVDF sensors.

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Section: Comparison Of the Approximate Energy Consumption Across Hard...supporting

confidence: 52%

Detection of Pipeline Leaks using BiopiezoelectricEnergy Harvesting Sensors via RecurrenceQuantification and Neuromorphic Computing

Rounak,

Banerjee,

Joseph

et al. 2024

Preprint

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“…For this purpose, we employed the Keras-Spiking framework to simulate the energy estimation of the CNN on the Intel-I7-4960X CPU and the Nvidia GTX Titan Black GPU, as well as the SNN on the Intel Loihi CPU (a neuromorphic chip). Our analysis is based on several assumptions by following the same conditions as in [46]. Table VI outlines the energy consumption for each method.…”

Section: Performance Comparison Between Cnns and Snnsmentioning

confidence: 99%

Ev-ReconNet: Visual Place Recognition Using Event Camera With Spiking Neural Networks

Lee

Hwang

2023

IEEE Sensors J.

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In this paper, we utilize the advantages of an event camera to tackle the visual place recognition (VPR) problem. The event camera's high measurement rate, low latency, and high dynamic range make it well-suited to overcome the limitations of conventional vision sensors. However, to apply the existing convolutional neural networks (CNNs) based algorithms such as NetVLAD, the asynchronous event stream should be converted to a synchronous image frame, which causes a loss in temporal information. To address this problem, this paper proposes a method that employs the asynchronous characteristic of spiking neural networks (SNNs) to leverage the temporal nature of event streams. The event stream is converted to event images and tensors in our preprocessing module. The SNNs-based reconstruction networks, which are converted from CNNs, reconstruct edge images from event tensors regardless of external environment changes. Visual place recognition is conducted by matching features of the database and those from NetVLAD, which we used as a feature extraction network in this study. To evaluate the performance of VPR by comparing the previous methods for DDD17 and the Brisbane-Event-VPR dataset. Experimental results demonstrate that the matching accuracy of the proposed method is better than previous methods, especially for datasets with adverse weather conditions. We also verify that the performance and energy efficiency are improved with SNNs over CNNs. Our code is available for download on https://github.com/AIRLABkhu/EvReconNet.

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“…performed during training [3], [4], an SNN has a simple structure that sends a spike when the action potential due to the accumulation of spikes exceeds a critical point which invokes a spike timing dependent plasticity (STDP) for feedforward style training [5]. This method does not require complicated feedback computations.…”

mentioning

confidence: 99%

A Digital Processing in Memory Architecture Using TCAM for Rapid Learning and Inference Based on a Spike Location Dependent Plasticity

Kim

Choi

et al. 2023

IEEE Access

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In this paper, we present a digital processing in memory (DPIM) configured as a stride edgedetection search frequency neural network (SE-SFNN) which is trained through a spike-location-dependent -plasticity (SLDP), a learning mechanism reminiscent of spike-timing-dependent plasticity. This mechanism allows for rapid online learning as well as a simple memory-based implementation. In particular, we employ a ternary data scheme to take advantage of a ternary content addressable memory (TCAM). The scheme utilizes a ternary representation of the image pixels and the TCAMs are used in a two-layer format to significantly reduce the computation time. The first layer applies several filtering kernels followed by the second layer that reorders pattern dictionaries of TCAMs to place the most frequent patterns at the top of each supervised TCAM dictionary. Numerous TCAM blocks in both layers operate in a massively parallel fashion using digital ternary values. There are no complicated multiply operations performed and learning is performed in a feedforward scheme. This allows rapid robust learning as a trade-off with the parallel memory block size. Furthermore, we propose a method to reduce the TCAM memory size using a two-tiered minor to major promotion (M2MP) of frequently occurring patterns. This reduction scheme is performed concurrently during the learning operation without incurring a preconditioning overhead. We show that with a minimal circuit overhead, the required memory size is reduced by 84.4% and the total clock cycles required for learning also decrease by 97.31 % while the accuracy decreases only by 1.12%. We classified images with 94.58% accuracy on the MNIST dataset. Using a 100MHz clock, our simulation results show that the MNIST training takes about 6.3 ms dissipating less than 4mW of average power. In terms of the inference speed, the trained hardware is capable of processing 5,882,352 images per second. INDEX TERMS Digital processing in memory, fast training, in memory computation, TCAM I. INTRODUCTIONRecently, various products using artificial intelligence algorithms have been released. Examples include home appliances such as refrigerators and air conditioners, automobiles for autonomous driving, and smart factories. These products collected data from edge devices and then sent the collected data to the server, where learning and computation were performed on the server [1]. However, in this case, there is a delay in the transmitting to the server and receiving the neural network weights. In addition, userspecific learning cannot be performed because it is not trained using only the data generated from the user's edge device. Due to these shortcomings, Artificial intelligence on things (AIoT) technology that can learn from edge devices was recently introduced [2].Unlike the existing convolutional neural networks (CNNs) and deep neural networks (DNNs) methods in which heavy amounts of iterative computation between tensors are

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An improved spiking network conversion for image classification

Cited by 8 publications

References 19 publications

Detection of Pipeline Leaks using BiopiezoelectricEnergy Harvesting Sensors via RecurrenceQuantification and Neuromorphic Computing

Detection of Pipeline Leaks using BiopiezoelectricEnergy Harvesting Sensors via RecurrenceQuantification and Neuromorphic Computing

Ev-ReconNet: Visual Place Recognition Using Event Camera With Spiking Neural Networks

A Digital Processing in Memory Architecture Using TCAM for Rapid Learning and Inference Based on a Spike Location Dependent Plasticity

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