23 papers
SPARX: Secure and Privacy-Aware Approximate CNN Acceleration with Edge RISC-V SoC
Sonu Kumar, Akash Sankhe, Mukul Lokhande +1
Edge-AI systems increasingly require real-time CNN inference under strict energy, performance, security, and privacy constraints. Approximate computing improves hardware efficiency…
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…
ADS-IMC: Accelerating Data Sorting with In-Memory Computation
Narendra Singh Dhakad, Santosh Kumar Vishvakarma
Sorting is a fundamental operation across numerous computational domains. Traditionally, this process involves transferring data from main memory to a processing unit for sorting,…
SRAM Based Digital Custom Compute Engine for Improved Area Efficiency of AI Hardware
Narendra Singh Dhakad, Santosh Kumar Vishvakarma
This paper presents a novel architecture utilizing a 10T SRAM cell for XNOR-based in-memory computing, aimed at mitigating the extensive routing challenges typically encountered in…
TREA: Low-precision Time-Multiplexed, Resource-Efficient Edge Accelerator for Object Detection and Classification
Vijay Pratap Sharma, Mukul Lokhande, Ratko Pilipovic +2
This work presents TREA, a low-precision time-multiplexed and resource-efficient edge-AI accelerator for object detection and classification, targeting stringent area-power-latency…
CARMEN: CORDIC-Accelerated Resource-Efficient Multi-Precision Inference Engine for Deep Learning
Sonu Kumar, Mukul Lokhande, Santosh Kumar Vishvakarma +1
This paper presents CARMEN, a runtime-adaptive, CORDIC-accelerated multi-precision vector engine for resource-efficient deep learning inference. The key insight is that CORDIC iter…