Memristors-based Neuromorphic Computing

The data-intensive nature of AI applications makes them computationally inefficient to run on traditional computing architectures. Due to the large number of vector-matrix multiplication operations in the Neural Networks, a significant amount of power is spent on data movement - to and from the memory. This is a latent limitation of von-Neumann Architecture, on which a majority of conventional computing-hardware is based. The latency of this data movement between the computing/processing unit and the memory also limits the throughput performance of the system.

A Memristor crossbar can solve this issue of increased energy consumption and latency by carrying out large amounts of vector-matrix multiplication operations in-memory. As an emerging Non-Volatile Memory (eNVM) technology, the memristor has gained immense popularity in recent years due to their ability to emulate spiking neurons and synapses to aid in the making of neuromorphic hardware. With the use of our two-layer fabricated VT memristor, we aim to develop energy-efficient Neuromorphic Computing architectures. Our work encompasses memristor-based Spiking Neural Networks, spiking Reservoir Computing, as well as in-memory computing architectures.

Currently invovled students

• Fabiha Nowshin
• Salekin