22 citations · 45 across the 27 of their papers we have counts for
12 papers · 1 filter
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…
EULER-ADAS: Energy-Efficient & SIMD-Unified Logarithmic-Posit Engine for Precision-Reconfigurable Approximate ADAS Acceleration
Mukul Lokhande, Ratko Pilipovic, Omkar Kokane +2
Advanced driver-assistance systems (ADAS) require neural compute engines that deliver low-latency inference under strict power and area constraints. Posit arithmetic is attractive…
DHFP-PE: Dual-Precision Hybrid Floating Point Processing Element for AI Acceleration
Shubham Kumar, Vijay Pratap Sharma, Vaibhav Neema +1
The rapid adoption of low-precision arithmetic in artificial intelligence and edge computing has created a strong demand for energy-efficient and flexible floating-point multiply-a…
L-SPINE: A Low-Precision SIMD Spiking Neural Compute Engine for Resource-efficient Edge Inference
Sonu Kumar, Mukul Lokhande, Santosh Kumar Vishvakarma
Spiking Neural Networks (SNNs) offer a promising solution for energy-efficient edge intelligence; however, their hardware deployment is constrained by memory overhead, inefficient…
CORVET: A CORDIC-Powered, Resource-Frugal Mixed-Precision Vector Processing Engine for High-Throughput AIoT applications
Sonu Kumar, Mohd Faisal Khan, Mukul Lokhande +1
This brief presents a runtime-adaptive, performance-enhanced vector engine featuring a low-resource, iterative CORDIC-based MAC unit for edge AI acceleration. The proposed design e…
SPADE: A SIMD Posit-enabled compute engine for Accelerating DNN Efficiency
Sonu Kumar, Lavanya Vinnakota, Mukul Lokhande +2
The growing demand for edge-AI systems requires arithmetic units that balance numerical precision, energy efficiency, and compact hardware while supporting diverse formats. Posit a…
Bio-RV: Low-Power Resource-Efficient RISC-V Processor for Biomedical Applications
Vijay Pratap Sharma, Annu Kumar, Mohd Faisal Khan +2
This work presents Bio-RV, a compact and resource-efficient RISC-V processor intended for biomedical control applications, such as accelerator-based biomedical SoCs and implantable…