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

Abstract

This brief presents a cortical neural pool (CNP) architecture incorporating a high-speed, resource-efficient CORDIC-based Hodgkin–Huxley (RCHH) neuron. The design employs modular CORDIC stages with a latency–area tradeoff and introduces a constraint-aware modular parallelism (CAMP) scheme with precision and stability handling. The FPGA implementation achieves 24.5% lower LUT utilization and 35.2% faster execution than prior designs while reducing normalized root-mean-square error (NRMSE) by 70%. The CNP engine provides 2.85× higher throughput (12.69 GOPS) than a functionally equivalent CORDIC-based DNN accelerator with only 0.35% accuracy degradation on MNIST. These results demonstrate a biologically accurate, resource-efficient cortical neural acceleration engine (NCE) that employs modular CORDIC stages with a latency–area tradeoff, making it suitable for resource-constrained edge-AI systems. The implementation is publicly available at https://github.com/mukullokhande99/CNP_RCHH © 1993-2012 IEEE.