Neuromorphic Computing with FPGA

Neuromorphic Computing is based on the non-von Newman architecture, which breaks the memory bottleneck of the traditional computing chips to achieve low-power, low-cost, and low-latency design. FPGA-based Neuromorphic Computing design focuses on the hardware implementations of neuromorphic computing systems and architectures on FPGA and the associated optimizations on it.

Due to the novel architecture of neuromorphic computing, it has better computation efficiency on temporal tasks than traditional Neural Networks such as Recurrent Neural Networks (RNNs). However, neuromorphic computing chips such as Intel’s Loihi, are still not mature enough to implement all kinds of circuits. Many ideas about the architectural optimizations on the neuromorphic computing systems need to be verified in time, which is easily doable on FPGAs - a reconfigurable and mature platform for circuit design.

We have been working on new architectural designs of the typical models of recurrent networks such as, Echo State Network (ESN), Delayed Feedback Reservoir (DFR), etc., adapting them to neuromorphic systems with their designs implemented and verified on the FPGA platforms.

Currently involved students

• Chunxiao (Charles) Lin
• Muhammad Farhan Azmine