Reconfigurable Neuromorphic VLSI Processor for On-Chip Real-Time Sensor Analytics

Abstract

On-chip analytics with extremely low-latency and energy-efficiency are required in the burgeoning area of intelligent sensor networks in healthcare, industrial automation and smart infrastructure. According to conventional processor architectures ( CPU, GPU, fixed-function accelerator) memory bottlenecks and high switching activity make them unable to satisfy hard constraints in real-time and power usage. In this piece of work, a reconfigurable neuromorphic VLSI processor is introduced with specific purpose of on-chip real time sensor analytics in edge environments. The proposed architecture affects an event-driven spiking neural core computation with a dynamically reconfigurable interconnect fabric and distributed on-chip memory subsystem that makes it possible to map adaptively to a heterogeneous workload using sensor streams. A design philosophy that focuses on hardware devices, less movement of data, small-scale storage of synapses, and part-time execution on its mode of operation connected with enhanced scalability and energy proportionality. The processor is an implementation of a CMOS VLSI design flow, and a reduction was more than twice in inference latency and energy consumption of the processor compared to a baseline using neuromorphic and FPGA technology, yet the processor remains responsive to multiple sensor modalities and in real time. The independent confirmation of experimental validation is done through use of biomedical and environmental sensor data sets to ensure stable throughput during dynamic workloads. The presented architecture provides a scalable and energy-conscious base of proposed next-generation embedded neuromorphic systems, providing them with practical implementation possibilities in wearable healthcare systems, independent IoT systems used in industries, and self-driving sensory nodes.