Abstract
Today, there is an ever increasing demand for embedding advanced intelligence
capabilities in energy- and area-constrained battery-powered devices such as
advanced wearables, on-body health monitoring systems, AR / VR headsets and
small robots such as drones. Therefore, there has been a surge in research effort
focusing on the use of Deep Neural Networks (DNNs) and their acceleration in
specialized hardware. However, these DNN solutions are still lagging far behind the remarkable efficacy of biological brains, such as, for example, in the honey
bee, which is capable of a large range of complex tasks while consuming only
10 μW of power. Hence, the study of event-driven Spiking Neural Networks
(SNNs) and local Hebbian plasticity rules (such as Spike-Timing-Dependent
Plastity or STDP) has attracted great attention in recent years, as a more
bio-plausible, neuromorphic model of computation, attempting to replicate in a
more faithful manner the biological neural mechanisms found in the brain. This
emerging AI field, termed Spiking Neuromorphic Computing, is seeking to take
direct inspiration from biology in order to enable a highly accurate execution of
complex AI tasks under the tightest of energy and chip area budgets, where
conventional DNNs would need significantly more energy and area.
capabilities in energy- and area-constrained battery-powered devices such as
advanced wearables, on-body health monitoring systems, AR / VR headsets and
small robots such as drones. Therefore, there has been a surge in research effort
focusing on the use of Deep Neural Networks (DNNs) and their acceleration in
specialized hardware. However, these DNN solutions are still lagging far behind the remarkable efficacy of biological brains, such as, for example, in the honey
bee, which is capable of a large range of complex tasks while consuming only
10 μW of power. Hence, the study of event-driven Spiking Neural Networks
(SNNs) and local Hebbian plasticity rules (such as Spike-Timing-Dependent
Plastity or STDP) has attracted great attention in recent years, as a more
bio-plausible, neuromorphic model of computation, attempting to replicate in a
more faithful manner the biological neural mechanisms found in the brain. This
emerging AI field, termed Spiking Neuromorphic Computing, is seeking to take
direct inspiration from biology in order to enable a highly accurate execution of
complex AI tasks under the tightest of energy and chip area budgets, where
conventional DNNs would need significantly more energy and area.
Original language | English |
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Publication status | Published - 2024 |
Externally published | Yes |