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One of the key innovations in neuromorphic computing, Spiking Neural Networks (SNNs) draw their inspiration from the biological nervous system, which can achieve remarkable efficiency and functionality. Unlike the Artificial Neural Networks (ANN) which rely on continuous-valued signals and is, therefore, computationally expensive, normal SNN works through discrete spikes of activity. This spike-based communication is very close to the neuronal activity existing in the biological brains and thus enables SNNs to process information in an event-driven, asynchronous manner. This approach saves an immense amount of power and increases the computational efficiency, thereby making SNNs suitable for real-time data processing with low-energy consumption applications, such as edge computing and IoT devices.
SNN core architecture, basically, forms a composition of interconnected neurons that are divided into layers, such as input, hidden, and output, where the neurons send communications with each other via spikes that represent the timing and intensity of signals. This communication through spikes enables SNN to be much more natural in processing temporal data, such as speech recognition, sensor data processing, and even complex recognition of any pattern in dynamic environments. SNNs also support online learning paradigms whereby the network adapts and learns progressively with new data, just as the biological brain has plasticity. It is fundamental for applications involving adaptive behavior as well as learning under changing conditions-in other words, autonomous vehicle or robotics.
Consumer electronics segment is expected to hold high market share during the forecast timeframe. The high market share is credited to growing adoption of neuromorphic chips in consumer drones and embedded systems. Neuromorphic computing plays a transformative role in consumer drones by enhancing their autonomous capabilities and real-time processing efficiency. The use of neuromorphic chips gives the ability to process the sensory data-like images and other environmental inputs-quicker and with less power compared to traditional computing methods. Such possibilities have enabled advanced functionalities that facilitate obstacle avoidance and precise navigation and adaptive flight control for better reliability and efficiency in applications like aerial photography, mapping, and surveillance for drones.
The North American market represents a large share of neuromorphic computing. Increasing government investment in AI technology expansion, significant advancements in research and development in neuromorphic computing and development of new technologies is driving the growth of the neuromorphic computing market in the region. The Canadian government’s focus on advancing artificial intelligence technology is expected to significantly boost the growth of neuromorphic computing in the coming years. A prime example is the introduction of the Artificial Intelligence and Data Act (AIDA) in June 2023. This legislation aims to mitigate the potential risks associated with AI, foster trust in Canada’s AI sector, and safeguard Canadians from various harms. By establishing stringent regulations and promoting ethical AI practices, AIDA positions Canada as a leader in responsible and trustworthy AI innovation. This supportive regulatory environment is likely to encourage further development and adoption of neuromorphic computing technologies, which are integral to the advancement of AI.
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