22 citations · 40 across the 21 of their papers we have counts for
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cs.NE2026
E-ReCON: An Energy- and Resource-Efficient Precision-Configurable Sparse nvCIM Macro for Conventional and Spiking Neural Edge Inference
Ankit Kumar Tenwar, Mukul Lokhande, Santosh Kumar Vishvakarma
This work presents E-ReCON, a 16 Kb energy and resource-efficient digital compute-in-memory (DCIM) macro based on a compact 3T1R ReRAM bitcell for edge-AI inference. The proposed b…
cs.NE2025
ReLANCE: A Resource-Efficient Low-Latency Cortical Neural Acceleration Engine
Sonu Kumar, Arjun S. Nair, Bhawna Chaudhary +2
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 appr…