# ReLANCE: A Resource-Efficient Low-Latency Cortical Neural Acceleration Engine

arXiv: https://arxiv.org/abs/2510.17392 · HTML: https://mlsanity.com/paper/2510.17392/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2025.

## Built on by (most cited first, 1 of 1)

- [L-SPINE: A Low-Precision SIMD Spiking Neural Compute Engine for Resource-efficient Edge Inference](https://mlsanity.com/paper/2604.03626/) (2026)

## Abstract (arXiv)

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. We introduce a novel Constraint-Aware Modular Parallelism (CAMP) with Precision & Stability handling to leverage maximum speedup and utilisation of hardware through hardware software co-design. 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) than a functionally equivalent CORDIC-based DNN engine, with only a 0.35% accuracy drop relative 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. The reproducibility codes are publicly available at https://github.com/mukullokhande99/CNP RCHH, facilitating rapid integration and further development by researchers.