Future VLSI Architectures Neuromorphic Computing is a M.Tech project topic for Electronics & Communication Engineering. It gives students a clear starting point for research, implementation planning, and documentation.
Future VLSI Architectures Neuromorphic Computing Project Details
| Abstract |
Because conventional CMOS scaling is hitting physical limits, we need a new approach to VLSI design. This research looks at combining bio‑inspired neuromorphic computing, edge artificial intelligence (AI), and sustainable electronic systems to meet today’s computational demands. We study spiking neural networks (SNNs) and hardware accelerators such as Eyeriss and EdgeTPU, evaluating energy‑aware architectures that provide high‑speed, low‑latency inference directly on the device. The work also examines emerging non‑volatile memory (NVM) technologies—Resistive RAM (ReRAM) and Phase‑Change Memory (PCM)—to enable in‑memory computing (IMC) that reduces data‑movement bottlenecks. We assess the environmental impact of these architectures and explore eco‑friendly materials like carbon nanotubes and organic semiconductors to create sustainable VLSI design frameworks.
Through systematic simulation and architectural modeling, we evaluate energy efficiency, computational throughput, and ecological viability. The resulting framework gives clear guidance for designing next‑generation, low‑power VLSI systems tailored for edge intelligence and sustainable computing environments.
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| Reference Paper |
Future VLSI Architectures for Neuromorphic Computing, Edge AI and Sustainable Systems |
| Domain |
VLSI Design |
| Sub-Domain |
VLSI & Embedded Systems / VLSI Design / Low-Power ASIC |
| PDF Download |
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