By mimicking the neurons and synapses of the human brain and employing spiking neural
networks on neuromorphic chips, neuromorphic computing offers a promising energy …

Neuromorphic Computing, a concept pioneered in the late 1980s, is receiving a lot of
attention lately due to its promise of reducing the computational energy, latency, as well as …

The explosion of “big data” applications imposes severe challenges of speed and scalability
on traditional computer systems. As the performance of traditional Von Neumann machines …

Recently, both industry and academia have proposed several different neuromorphic
systems to execute machine learning applications that are designed using Spiking Neural …

Driven by the expanse of Internet of Things (IoT) and Cyber-Physical Systems (CPS), there
is an increasing demand to process streams of temporal data on embedded devices with …

Guided by brain-like 'spiking'computational frameworks, neuromorphic computing—brain-
inspired computing for machine intelligence—promises to realize artificial intelligence while …

Neuromorphic architectures have been introduced as platforms for energy-efficient spiking
neural network execution. The massive parallelism offered by these architectures has also …

Neuromorphic hardware architectures represent a growing family of potential post-Moore's
Law Era platforms. Largely due to event-driving processing inspired by the human brain …

Neuromorphic computing is a non-von Neumann computing paradigm that performs
computation by emulating the human brain. Neuromorphic systems are extremely energy …

SENeCA is our first RISC-V-based digital neuromorphic processor to accelerate bio-inspired
Spiking Neural Networks for extreme edge applications inside or near sensors where ultra …