To address the limitations of GPUs for AI, engineers are exploring general-purpose hardware, dedicated DL hardware and neuromorphic hardware.
neuromorphic
The brain-inspired architecture gives neuromorphic systems distinct advantages, particularly for edge computing applications in consumer devices and industrial IoT.
Neuromorphic computing represents a transformative approach to artificial intelligence, seeking to emulate the human brain's neural structures and processing methods. This computing paradigm offers significant advancements in efficiency and performance for specific tasks, including those requiring real-time processing and low power consumption. Here, we explore the algorithms that drive neuromorphic computing, its potential use cases, and its diverse applications. Algorithms in Neuromorphic Computing Neuromorphic computing leverages unique algorithms to mimic neurobiological architectures inherent to the nervous system. Key algorithms include: Spiking Neural Networks (SNNs): Unlike traditional neural networks, which process continuous data, SNNs manage binary events or 'spikes.' They are