Muhammad Ihsan Al Hafiz

Due to the rise of Artificial Intelligence, energy consumption is expected to rise. A brain as a source of inspiration actually requires ~20 W to work. Is it because of the current AI algorithm? Or a computation machine that is not compatible? Or both?

My research is to explore the neuromorphic computing, or how we do computation more biologically close to the brain, in a custom computation machine that is more compatible with it. Currently, I am working with two biologically plausible algorithms, Bayesian Confidence Propagation Neural Networks (BCPNNs) and Spiking Neural Networks (SNNs). BCPNN draws inspiration from the columnar structure of the brain and uses Bayesian statistics for its learning mechanism. While SNN represents the computation more like the brain, as spikes signal. Due to the specific algorithm of neuromorphic computing, general-purpose machines like the Central Processing Unit (CPU) or the Graphic Processing Unit (GPU) will not be able to optimize low-energy implementation. Therefore, a specialized hardware accelerator is needed as an alternative to a general-purpose computing machine for neuromorphic computing.  The target is to replace the current AI workload with more efficient, low-energy-consuming neuromorphic computing to support a more sustainable future.

My research interests include hardware accelerators, neuromorphic computing, High-Performance Computing, FPGAs, and reconfigurable computing. More information about me can be found at ihsanalhafiz.github.io.