Neuromorphic Computing

Technology Overview

Neuromorphic computing combined with FPGA acceleration enables energy-efficient, scalable, and adaptive AI at the edge. Leveraging the Kria KV260 MPSoC and a neuromorphic IP core, spiking neural networks (SNNs) are implemented for efficient pattern recognition, low-power operation, and real-time processing.

A demonstration highlights handwritten digit classification using the MNIST dataset, streamed from a USB camera to the FPGA and displayed in real time.

Classification results are transmitted via the Mi-Nexa (6LoWPAN) wireless protocol to a gateway and visualised on a cloud dashboard. This proof-of-concept illustrates the versatility and real-world applicability of scalable neuromorphic edge AI across broader domains.

Neuromorphic Computing is the brain-inspired future of processing. Utilizing Spiking Neural Networks (SNNs) , it enables ultra-low-power, adaptive AI at the Edge , delivering complex, real-time pattern recognition with a fraction of the power of traditional computing.

Key Features

• Low Power Consumption

  The neuromorphic architecture mimics the human brain’s energy efficiency, making it well-suited for battery-powered or remote applications. An MNIST demonstration highlights how the technology can classify data with minimal power usage.

• Real-Time Processing

  FPGA technology enables instant inference, crucial for latency-sensitive applications. The demo unit enables instant digit recognition directly from the camera input, demonstrating its real-time capability.

• Wireless Connectivity

  The demo unit transmits classification results wirelessly to a gateway and cloud dashboard. This feature demonstrates how the system can communicate efficiently with other devices and monitoring platforms.

• Integrated Simulation & Deployment

  Models are trained and tested in the RANC simulator, then deployed seamlessly to FPGA hardware.

• Adaptive & Scalable Design

  Architecture can be scaled from simple digit recognition to more complex AI tasks such as license plate or biosensor data analysis.

Technology Benefits

• Efficiency

  By mimicking the brain’s parallel processing, the system performs tasks like digit recognition with a fraction of the energy of traditional computing methods (CPUs/GPUs).

• Adaptability

  The technology learns to recognise different pattern. For instance, the system can be trained to recognise a variety of handwritten styles, showcasing its ability to adapt to data variations.

• Scalability

  The underlying architecture can be efficiently scaled from simple tasks like digit

The Neuromorphic and FPGA for Edge AI project explores the ground-breaking potential of Neuromorphic Computing in Edge AI (Artificial Intelligence) applications. Neuromorphic Computing represents a paradigm shift in computational methods, drawing inspiration from the human brain’s functioning to deliver energy-efficient and adaptive AI solutions. At the core of this initiative is the Reconfigurable Architecture for Neuromorphic Computing (RANC), a cutting-edge concept poised to transform AI processing.