A 218 GOPS neural network accelerator based on a novel cost-efficient surrogate gradient scheme for pattern classification

Siddique, Ali; Iqbal, Muhammad Azhar; Aleem, Muhammad; Islam, Muhammad Arshad

doi:10.1016/j.micpro.2023.104831

Cited by 3 publications

(4 citation statements)

References 33 publications

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“…Furthermore, we focus not only on the algorithm but also on the hardware design. This approach has been proven to be extremely effective at developing high-performance systems [9,22] since hardware efficiency is directly related to algorithmic efficiency.…”

Section: Related Work and Problem Definitionmentioning

confidence: 99%

“…They implement fully parallel sigmoidal neurons using look-up tables on an FPGA. A generic hardware NN system that has been tested on a digit classification dataset is presented in [22]. The system in [34] has been tested on a cancer detection dataset.…”

Section: Neuromorphic Accelerators (Nmas)mentioning

confidence: 99%

“…The structure of output-layer NACs is shown in Figure 12. Since softmax is a costly function and is used only when cross entropy loss is to be visualized, we use logits to perform prediction at the output [22]. The maximum voltage neuron corresponds to the predicted class.…”

Section: Output-layer Edcsmentioning

confidence: 99%

“…The system in [56] implements a network with radial basis functions just to demonstrate the efficiency of slope-based Gaussian approximation; no dataset is used to evaluate performance. The system in [22] uses ReLU at all the network layers to to improve classification accuracy. However, it uses extremely small weights and biases, which might not be enough for obtaining a reasonable accuracy on aquaponics data.…”

Section: Hardware Efficiency Evaluation and Comparisonsmentioning

confidence: 99%

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SpikoPoniC: A Low-Cost Spiking Neuromorphic Computer for Smart Aquaponics

Siddique,

Sun,

Hou

et al. 2023

Agriculture

Self Cite

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Aquaponics is an emerging area of agricultural sciences that combines aquaculture and hydroponics in a symbiotic way to enhance crop production. A stable smart aquaponic system requires estimating the fish size in real time. Though deep learning has shown promise in the context of smart aquaponics, most smart systems are extremely slow and costly and cannot be deployed on a large scale. Therefore, we design and present a novel neuromorphic computer that uses spiking neural networks (SNNs) for estimating not only the length but also the weight of the fish. To train the SNN, we present a novel hybrid scheme in which some of the neural layers are trained using direct SNN backpropagation, while others are trained using standard backpropagation. By doing this, a blend of high hardware efficiency and accuracy can be achieved. The proposed computer SpikoPoniC can classify more than 84 million fish samples in a second, achieving a speedup of at least 3369× over traditional general-purpose computers. The SpikoPoniC consumes less than 1100 slice registers on Virtex 6 and is much cheaper than most SNN-based hardware systems. To the best of our knowledge, this is the first SNN-based neuromorphic system that performs smart real-time aquaponic monitoring.

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Section: Related Work and Problem Definitionmentioning

confidence: 99%

Section: Neuromorphic Accelerators (Nmas)mentioning

confidence: 99%

Section: Output-layer Edcsmentioning

confidence: 99%

Section: Hardware Efficiency Evaluation and Comparisonsmentioning

confidence: 99%

See 2 more Smart Citations

SpikoPoniC: A Low-Cost Spiking Neuromorphic Computer for Smart Aquaponics

Siddique,

Sun,

Hou

et al. 2023

Agriculture

Self Cite

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Efficient Training of Spiking Neural Networks with Multi-parallel Implicit Stream Architecture

Cao,

Li,

Wang

et al. 2024

Lecture Notes in Computer Science

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N-AquaRAM: A Cost-Efficient Deep Learning Accelerator for Real-Time Aquaponic Monitoring

Siddique,

Iqbal,

Sun

et al. 2024

Agric Res

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Aquaponics is an emerging area of agricultural sciences that combines aquaculture and hydroponics in a symbiotic way to increase crop production. Though it offers a lot of advantages over traditional techniques, including chemical-free and soil-less farming, its commercial application suffers from some problems such as the lack of experienced manpower. To operate a stable smart aquaponic system, it is critical to estimate the fish size properly. In this context, the use of dedicated hardware for real-time aquaponic monitoring can greatly resolve the issue of inexperienced handlers. In this article, we present a complete methodology to train a deep neural network to perform fish size estimation in real time. To achieve high accuracy, a novel implementation of swish function is presented. This novel version is far more hardware efficient than the original one, while being extremely accurate. Moreover, we present a deep learning accelerator that can classify 40 million fish samples in a second. The dedicated real-time system is about 1600 times faster than the one based on general-purpose computers. The proposed neuromorphic accelerator consumes about 2600 slice registers on a low-end model of Virtex 6 FPGA series.

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A 218 GOPS neural network accelerator based on a novel cost-efficient surrogate gradient scheme for pattern classification

Cited by 3 publications

References 33 publications

SpikoPoniC: A Low-Cost Spiking Neuromorphic Computer for Smart Aquaponics

SpikoPoniC: A Low-Cost Spiking Neuromorphic Computer for Smart Aquaponics

Efficient Training of Spiking Neural Networks with Multi-parallel Implicit Stream Architecture

N-AquaRAM: A Cost-Efficient Deep Learning Accelerator for Real-Time Aquaponic Monitoring

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