Neuromorphic Computing

Abstract

Neuromorphic computing represents a
cutting-edge approach to computer engineering, inspired
by the architecture and functionality of the human brain
and nervous system. Unlike traditional computing
paradigms, neuromorphic computing seeks to emulate
the parallel processing and distributed memory
capabilities of biological neural networks.
The need for neuromorphic computing arises from
inherent limitations in conventional computing
architectures, such as the von Neumann design. In
traditional systems, memory and computation are
segregated, requiring data to be transferred between
memory and the central processing unit (CPU) via a bus.
However, the speed of memory access and data transfer
has not kept pace with the increasing performance of
CPUs, leading to inefficiencies known as the von
Neumann bottleneck and the computation-memory gap.
Neuromorphic computing addresses these challenges by
adopting a fundamentally different approach. Inspired
by the brain's ability to process information in parallel
and store data locally within neurons, neuromorphic
systems utilize artificial neurons that communicate
through electric signals, or spikes. This enables them to
perform computations and store information
simultaneously, without the need for separate memory
and processing units.
Key components of neuromorphic computing include
artificial neurons, which mimic the behavior of
biological neurons, and electric spikes, which serve as
the means of communication between neurons. By
leveraging the principles of the nervous system,
neuromorphic computing offers the potential for
enhanced efficiency, scalability, and performance,
particularly in tasks involving large-scale data
processing and pattern recognition.