ReLACE: A Resource-Efficient Low-Latency Cortical Acceleration Engine

• URL: http://arxiv.org/abs/2510.17392v1
• Date: Mon, 20 Oct 2025 10:33:50 GMT
• Title: ReLACE: A Resource-Efficient Low-Latency Cortical Acceleration Engine
• Authors: Sonu Kumar, Arjun S. Nair, Bhawna Chaudhary, Mukul Lokhande, Santosh Kumar Vishvakarma,
• Abstract summary: We present a Cortical Neural Pool architecture featuring a CORDIC-based Hodgkin Huxley (RCHH) neuron model.<n>The FPGA implementation of the RCHH neuron shows 24.5% LUT reduction and 35.2% improved speed.<n>The design shows biologically accurate, low-resource spiking neural network implementations for resource-constrained edge AI applications.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a Cortical Neural Pool (CNP) architecture featuring a high-speed, resource-efficient CORDIC-based Hodgkin Huxley (RCHH) neuron model. Unlike shared CORDIC-based DNN approaches, the proposed neuron leverages modular and performance-optimised CORDIC stages with a latency-area trade-off. The FPGA implementation of the RCHH neuron shows 24.5% LUT reduction and 35.2% improved speed, compared to SoTA designs, with 70% better normalised root mean square error (NRMSE). Furthermore, the CNP exhibits 2.85x higher throughput (12.69 GOPS) compared to a functionally equivalent CORDIC-based DNN engine, with only a 0.35% accuracy drop compared to the DNN counterpart on the MNIST dataset. The overall results indicate that the design shows biologically accurate, low-resource spiking neural network implementations for resource-constrained edge AI applications.

Related papers

• Assessing Neuromorphic Computing for Fingertip Force Decoding from Electromyography [0.09999629695552194]
  High-density surface electromyography (HD-sEMG) provides a noninvasive neural interface for assistive and rehabilitation control.<n>We assess a spiking neural network (SNN) as a neuromorphic architecture against a temporal convolutional network (TCN) for decoding fingertip force from motor-unit firing.
  arXiv  Detail & Related papers  (2025-12-11T00:33:31Z)
• Deep-Unrolling Multidimensional Harmonic Retrieval Algorithms on Neuromorphic Hardware [78.17783007774295]
  This paper explores the potential of conversion-based neuromorphic algorithms for highly accurate and energy-efficient single-snapshot multidimensional harmonic retrieval.<n>A novel method for converting the complex-valued convolutional layers and activations into spiking neural networks (SNNs) is developed.<n>The converted SNNs achieve almost five-fold power efficiency at moderate performance loss compared to the original CNNs.
  arXiv  Detail & Related papers  (2024-12-05T09:41:33Z)
• Decoding finger velocity from cortical spike trains with recurrent spiking neural networks [6.404492073110551]
  Invasive brain-machine interfaces (BMIs) can significantly improve the life quality of motor-impaired patients. BMIs must meet strict latency and energy constraints while providing reliable decoding performance. We trained RSNNs to decode finger velocity from cortical spike trains of two macaque monkeys.
  arXiv  Detail & Related papers  (2024-09-03T10:15:33Z)
• NAS-BNN: Neural Architecture Search for Binary Neural Networks [55.058512316210056]
  We propose a novel neural architecture search scheme for binary neural networks, named NAS-BNN. Our discovered binary model family outperforms previous BNNs for a wide range of operations (OPs) from 20M to 200M. In addition, we validate the transferability of these searched BNNs on the object detection task, and our binary detectors with the searched BNNs achieve a novel state-of-the-art result, e.g., 31.6% mAP with 370M OPs, on MS dataset.
  arXiv  Detail & Related papers  (2024-08-28T02:17:58Z)
• High-performance deep spiking neural networks with 0.3 spikes per neuron [9.01407445068455]
  It is hard to train biologically-inspired spiking neural networks (SNNs) than artificial neural networks (ANNs) We show that training deep SNN models achieves the exact same performance as that of ANNs. Our SNN accomplishes high-performance classification with less than 0.3 spikes per neuron, lending itself for an energy-efficient implementation.
  arXiv  Detail & Related papers  (2023-06-14T21:01:35Z)
• A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural Networks [53.31941519245432]
  Brain-inspired spiking neural networks (SNNs) have demonstrated promising capabilities in solving pattern recognition tasks. These SNNs are grounded on homogeneous neurons that utilize a uniform neural coding for information representation. In this study, we argue that SNN architectures should be holistically designed to incorporate heterogeneous coding schemes.
  arXiv  Detail & Related papers  (2023-05-26T02:52:12Z)
• A Hybrid ANN-SNN Architecture for Low-Power and Low-Latency Visual Perception [27.144985031646932]
  Spiking Neural Networks (SNN) are a class of bio-inspired neural networks that promise to bring low-power and low-latency inference to edge devices. We show for the task of event-based 2D and 3D human pose estimation that our method consumes 88% less power with only a 4% decrease in performance compared to its fully ANN counterparts.
  arXiv  Detail & Related papers  (2023-03-24T17:38:45Z)
• Ultra-low Latency Adaptive Local Binary Spiking Neural Network with Accuracy Loss Estimator [4.554628904670269]
  We propose an ultra-low latency adaptive local binary spiking neural network (ALBSNN) with accuracy loss estimators. Experimental results show that this method can reduce storage space by more than 20 % without losing network accuracy.
  arXiv  Detail & Related papers  (2022-07-31T09:03:57Z)
• Can Deep Neural Networks be Converted to Ultra Low-Latency Spiking Neural Networks? [3.2108350580418166]
  Spiking neural networks (SNNs) operate via binary spikes distributed over time. SOTA training strategies for SNNs involve conversion from a non-spiking deep neural network (DNN) We propose a new training algorithm that accurately captures these distributions, minimizing the error between the DNN and converted SNN.
  arXiv  Detail & Related papers  (2021-12-22T18:47:45Z)
• Sub-bit Neural Networks: Learning to Compress and Accelerate Binary Neural Networks [72.81092567651395]
  Sub-bit Neural Networks (SNNs) are a new type of binary quantization design tailored to compress and accelerate BNNs. SNNs are trained with a kernel-aware optimization framework, which exploits binary quantization in the fine-grained convolutional kernel space. Experiments on visual recognition benchmarks and the hardware deployment on FPGA validate the great potentials of SNNs.
  arXiv  Detail & Related papers  (2021-10-18T11:30:29Z)
• ANNETTE: Accurate Neural Network Execution Time Estimation with Stacked Models [56.21470608621633]
  We propose a time estimation framework to decouple the architectural search from the target hardware. The proposed methodology extracts a set of models from micro- kernel and multi-layer benchmarks and generates a stacked model for mapping and network execution time estimation. We compare estimation accuracy and fidelity of the generated mixed models, statistical models with the roofline model, and a refined roofline model for evaluation.
  arXiv  Detail & Related papers  (2021-05-07T11:39:05Z)
• Strengthening the Training of Convolutional Neural Networks By Using Walsh Matrix [0.0]
  We have modified the training and structure of DNN to increase the classification performance. A minimum distance network (MDN) following the last layer of the convolutional neural network (CNN) is used as the classifier. In different areas, it has been observed that a higher classification performance was obtained by using the DivFE with less number of nodes.
  arXiv  Detail & Related papers  (2021-03-31T18:06:11Z)
• Neural Architecture Search For LF-MMI Trained Time Delay Neural Networks [61.76338096980383]
  A range of neural architecture search (NAS) techniques are used to automatically learn two types of hyper- parameters of state-of-the-art factored time delay neural networks (TDNNs) These include the DARTS method integrating architecture selection with lattice-free MMI (LF-MMI) TDNN training. Experiments conducted on a 300-hour Switchboard corpus suggest the auto-configured systems consistently outperform the baseline LF-MMI TDNN systems.
  arXiv  Detail & Related papers  (2020-07-17T08:32:11Z)
• You Only Spike Once: Improving Energy-Efficient Neuromorphic Inference to ANN-Level Accuracy [51.861168222799186]
  Spiking Neural Networks (SNNs) are a type of neuromorphic, or brain-inspired network. SNNs are sparse, accessing very few weights, and typically only use addition operations instead of the more power-intensive multiply-and-accumulate operations. In this work, we aim to overcome the limitations of TTFS-encoded neuromorphic systems.
  arXiv  Detail & Related papers  (2020-06-03T15:55:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.