AI, Neuromorphic, Advanced Computing Hardware

Overview

Emerging technologies in Artificial Intelligence, like neuromorphic systems and advanced computing hardware, are redefining the future of computing. While CPUs support general-purpose tasks and GPUs/NPUs accelerate contemporary AI workloads, the energy demands of large-scale models are rapidly increasing. Neuromorphic hardware—built on brain-inspired principles such as sparsity, asynchronous event-driven architectures, and the purposeful exploitation of noise—offers a transformative solution. These systems deliver low-power, real-time AI performance that can be of the order of magnitude faster than conventional approaches. Realizing this potential will require innovation across hardware, circuits, devices, and materials to drive the next era of semiconductor design and manufacturing.

Success Stories

To learn more about the interesting work and success stories in the field of AI/Neuromorphic Advanced Computing across India, visit our Success Stories page. Link

Technical Committee members

Dr. Chetan Singh Thakur Academic Expert IISc Bengaluru	Dr. Chetan Singh Thakur is an Associate Professor at DESE, IISc Bengaluru. His expertise lies in neuromorphic computing, FPGA & mixed-signal VLSI systems, computational neuroscience, and machine learning for edge-computing. Profile
Dr. Manan Suri Academic Expert IIT Delhi	Dr. Manan Suri leads the NVM and Neuromorphic Hardware Research group at IIT-Delhi. His work centers on advanced non-volatile memory technologies including RRAM, PCM, MRAM, and their integration into neuromorphic and in-memory computing architectures. Profile
Dr. Laxmeesha Somappa Academic Expert IIT Bombay	Dr. Laxmeesha Somappa is an Assistant Professor at EE, IIT Bombay, specializing in low-power analog, digital, and mixed-signal IC design for biomedical applications. His research spans neuromodulation SoCs, delta-sigma data converters, MEMS and sensor interfaces, with prior industry and academic experience in audio ASICs, Flash memory hardware, and neuroprosthetic systems. Profile
Dr. Shaibal Mukherjee Academic Expert IIT Indore	Dr. Shaibal Mukherjee is an Associate Professor at IIT Indore, focusing on the fabrication and characterization of oxide-based memristive devices and RRAM for neuromorphic computing. His work includes developing high-density crossbar arrays and demonstrating synaptic learning rules for in-memory and brain-inspired computing. Profile
Dr. Shubham Sahay Academic Expert IIT Kanpur	Dr. Shubham Sahay's is an Assistant Professor at EE, IIT Kanpur. His research focuses on developing hardware for neuromorphic computing and security, utilizing novel device architectures to scale CMOS technology. His work includes the analytical and compact modeling of semiconductor devices, with a specific interest in non-volatile memories and spintronics. Profile
Dr. Alex James Academic Expert Digital University Kerala	Dr. A. P. James is a Professor at Digital University Kerala and CTO at Graphene Aurora, focusing on graphene-based neural chips and memristive crossbar arrays for AI applications. His work also encompasses the design of advanced RF circuits and a wide range of antenna types, alongside research into 2D materials and next-generation memory technologies like ReRAMs and STTRAM. Profile
Dr. Sumedh Risbud Industry Expert Intel Labs	Dr. Sumedh Risbud is a Research Scientist at Intel Labs and a key contributor to the Loihi neuromorphic chip project. His research focuses on developing algorithms and architectures for neuromorphic hardware, including methods for solving complex optimization problems on chips like Loihi 2. He works on applications for size-, weight-, and power-constrained (SWaP) autonomous systems, demonstrating significant energy-delay product reductions compared to conventional CPUs and GPUs. Profile
Sounak Dey Industry Expert TCS Research	Sounak Dey is a Senior Scientist at TCS Research where he leads work on industry-focused neuromorphic computing solutions. His research centers on developing and deploying Spiking Neural Network (SNN) models on low-power neuromorphic hardware for real-time edge AI applications. Profile
Rahul Rao Industry Expert IBM	Rahul Rao is a Distinguished Engineer at IBM's India Systems Development Lab, where he leads processor design for AI-optimized hardware. His work focuses on developing on-chip AI accelerators, such as for the Telum II processor, designed for real-time, large-scale enterprise workloads. Profile
Dr. Udayan Ganguly Academic Expert IIT Bombay	Dr. Udayan Ganguly is a Professor in the Department of Electrical Engineering at IIT Bombay, where his research focuses on semiconductor device physics and processing for advanced memory and neuromorphic systems. His work includes developing complex metal oxide RRAM for in-memory computing, three-terminal RRAM architectures, and SOI-based neurons for spiking neural networks. Profile