Mukul Lokhande

According to our database, Mukul Lokhande authored at least 27 papers between 2024 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

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

Sonu Kumar, Arjun S. Nair, Bhawna Chaudhary, Mukul Lokhande, Santosh Kumar Vishvakarma

IEEE Trans. Very Large Scale Integr. Syst., June, 2026

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

CoRR, May, 2026

TREA: Low-precision Time-Multiplexed, Resource-Efficient Edge Accelerator for Object Detection and Classification.

Vijay Pratap Sharma, Mukul Lokhande, Ratko Pilipovic, Omkar Kokane, Santosh Kumar Vishvakarma

CoRR, May, 2026

CARMEN: CORDIC-Accelerated Resource-Efficient Multi-Precision Inference Engine for Deep Learning.

Sonu Kumar, Mukul Lokhande, Santosh Kumar Vishvakarma, Adam Teman

CoRR, May, 2026

EULER-ADAS: Energy-Efficient & SIMD-Unified Logarithmic-Posit Engine for Precision-Reconfigurable Approximate ADAS Acceleration.

Mukul Lokhande, Ratko Pilipovic, Omkar Kokane, Adam Teman, Santosh Kumar Vishvakarma

CoRR, May, 2026

L-SPINE: A Low-Precision SIMD Spiking Neural Compute Engine for Resource-efficient Edge Inference.

Sonu Kumar, Mukul Lokhande, Santosh Kumar Vishvakarma

CoRR, April, 2026

CORVET: A CORDIC-Powered, Resource-Frugal Mixed-Precision Vector Processing Engine for High-Throughput AIoT applications.

Sonu Kumar, Mohd Faisal Khan, Mukul Lokhande, Santosh Kumar Vishvakarma

CoRR, February, 2026

SPADE: A SIMD Posit-enabled compute engine for Accelerating DNN Efficiency.

Sonu Kumar, Lavanya Vinnakota, Mukul Lokhande, Santosh Kumar Vishvakarma, Adam Teman

CoRR, January, 2026

Bio-RV: Low-Power Resource-Efficient RISC-V Processor for Biomedical Applications.

Vijay Pratap Sharma, Annu Kumar, Mohd Faisal Khan, Mukul Lokhande, Santosh Kumar Vishvakarma

CoRR, January, 2026

RAMAN: Resource-Efficient ApproxiMate Posit Processing for Algorithm-Hardware Co-DesigN.

Mohd Faisal Khan, Mukul Lokhande, Santosh Kumar Vishvakarma

Proceedings of the 39th International Conference on VLSI Design & 25th International Conference on Embedded Systems, 2026

XR-NPE: High-Throughput Mixed-Precision SIMD Neural Processing Engine for Extended Reality Perception Workloads.

Tejas Chaudhari, Akarsh J, Tanushree Dewangan, Mukul Lokhande, Santosh Kumar Vishvakarma

Proceedings of the 39th International Conference on VLSI Design & 25th International Conference on Embedded Systems, 2026

Bhasha-Rupantarika: Algorithm-Hardware Co-design approach for Multilingual Neural Machine Translation.

Mukul Lokhande, Tanushree Dewangan, Mohd Sharik Mansoori, Tejas Chaudhari, Akarsh J, Damayanti Lokhande, Adam Teman, Santosh Kumar Vishvakarma

Proceedings of the 27th International Symposium on Quality Electronic Design, 2026

2025

FERMI-ML: A Flexible and Resource-Efficient Memory-In-Situ SRAM Macro for TinyML acceleration.

Mukul Lokhande, Akash Sankhe, S. V. Jaya Chand, Santosh Kumar Vishvakarma

CoRR, November, 2025

Res-DPU: Resource-shared Digital Processing-in-memory Unit for Edge-AI Workloads.

Mukul Lokhande, Narendra Singh Dhakad, Seema Chouhan, Akash Sankhe, Santosh Kumar Vishvakarma

CoRR, October, 2025

ReLACE: A Resource-Efficient Low-Latency Cortical Acceleration Engine.

Sonu Kumar, Arjun S. Nair, Bhawna Chaudhary, Mukul Lokhande, Santosh Kumar Vishvakarma

CoRR, October, 2025

Flex-PE: Flexible and SIMD Multiprecision Processing Element for AI Workloads.

Mukul Lokhande, Gopal Raut, Santosh Kumar Vishvakarma

IEEE Trans. Very Large Scale Integr. Syst., June, 2025

POLARON: Precision-aware On-device Learning and Adaptive Runtime-cONfigurable AI acceleration.

Mukul Lokhande, Santosh Kumar Vishvakarma

CoRR, June, 2025

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine.

Anushka Jha, Tanushree Dewangan, Mukul Lokhande, Santosh Kumar Vishvakarma

CoRR, June, 2025

CORDIC Is All You Need.

Omkar Kokane, Adam Teman, Anushka Jha, Guru Prasath SL, Gopal Raut, Mukul Lokhande, S. V. Jaya Chand, Tanushree Dewangan, Santosh Kumar Vishvakarma

CoRR, March, 2025

Retrospective: A CORDIC Based Configurable Activation Function for NN Applications.

Omkar Kokane, Gopal Raut, Salim Ullah, Mukul Lokhande, Adam Teman, Akash Kumar, Santosh Kumar Vishvakarma

Proceedings of the IEEE Computer Society Annual Symposium on VLSI, 2025

Area-Optimized 2D Interleaved Adder Tree Design for Sparse DCIM Edge Processing.

Akash Sankhe, Mukul Lokhande, Radheshyam Sharma, Santosh Kumar Vishvakarma

Proceedings of the 26th International Symposium on Quality Electronic Design, 2025

LPRE: Logarithmic Posit-enabled Reconfigurable edge-AI Engine.

Omkar Kokane, Mukul Lokhande, Gopal Raut, Adam Teman, Santosh Kumar Vishvakarma

Proceedings of the IEEE International Symposium on Circuits and Systems, 2025

HYDRA: A Resource-Efficient Hybrid Data-Multiplexed, Run-time Layer-Reconfigurable Compute Engine for DNN Acceleration.

Sonu Kumar, Komal Gupta, Isuru S. Dasanayake, Mukul Lokhande, Santosh Kumar Vishvakarma

Proceedings of the 2025 IEEE 19th International Conference on Industrial and Information Systems (ICIIS), 2025

A NOR8T SRAM Digital Compute-in-Memory Macro for Sparse and Scalable Edge-AI Processing.

Pratham Sharma, Mukul Lokhande, Akash Sankhe, Kwok Tai Chui, Brij Bhooshan Gupta, Santosh Kumar Vishvakarma

Proceedings of the IEEE Asia Pacific Conference on Circuits and Systems, 2025

2024

Flex-PE: Flexible and SIMD Multi-Precision Processing Element for AI Workloads.

Mukul Lokhande, Gopal Raut, Santosh Kumar Vishvakarma

CoRR, 2024

HYDRA: Hybrid Data Multiplexing and Run-time Layer Configurable DNN Accelerator.

Sonu Kumar, Komal Gupta, Gopal Raut, Mukul Lokhande, Santosh Kumar Vishvakarma

CoRR, 2024

HOAA: Hybrid Overestimating Approximate Adder for Enhanced Performance Processing Engine.

Omkar Kokane, Prabhat Sati, Mukul Lokhande, Santosh Kumar Vishvakarma

Proceedings of the 28th International Symposium on VLSI Design and Test, 2024