| Περίληψη: | Spiking Neural Networks (SNNs) have gained attention in recent years as a potential solution for low-power embedded systems, as they offer a more energy-efficient alternative but also effective to classical Artificial Neural Networks (ANNs). SNNs differ from ANNs in that they incorporate the time factor into their computations and encode information through the timing or frequency of spikes. The event-driven nature of SNNs makes them ideal for implementation in neuromorphic hardware systems, leading to even greater energy efficiency compared to ANNs.
The challenge with SNNs is that they require a different computational model and implementation compared to ANNs. This creates new design challenges in building large-scale networks and requires different architectures. To address these challenges and facilitate the development of SNNs for machine learning applications, a spiking accelerator has been proposed.
The goal of the spiking accelerator is to make it easier and faster to develop SNNs for machine learning applications that are traditionally addressed by ANNs. It aims to help close the accuracy gap between SNNs and ANNs, rather than serving as a general simulator for biologically inspired neuron models. By making it easier to develop SNNs, the spiking accelerator has the potential to bring the benefits of more energy-efficient computing systems to a wider range of applications.
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