Neuromorphic computing combines biology, electrical engineering, computer science, and mathematics technologies to create artificial neural systems. It encompasses the production and use of neural networks, proving the efficiency of the concept of how the brain performs its functions. In the current state of technological landscape, as AI has been advanced technology, neuromorphic chips have several things in common with AI. Both are aimed at processing artificial neural networks (ANN), and offering improvements in performance.
The architecture of a neuromorphic device involves the development of components whose functions imitate aspects of the brain’s architecture and dynamics to replicate its functional capabilities in terms of computational power, dynamic learning and energy efficiency. Companies like Intel, IBM, Qualcomm, among others are increasingly involved to develop neuromorphic computers. In this context, Intel Labs is driving computer-science research that contributes to the third generation of AI. The research’s key focus areas include neuromorphic computing, which is concerned with emulating the neural structure and operation of the human brain, as well as probabilistic computing that creates algorithmic approaches to addressing the uncertainty, ambiguity, and contradiction in the natural world.
According to a research paper by a scientist at Intel Charles Augustine who predicts neuromorphic chips will be able to handle AI tasks such as cognitive computing, adaptive artificial intelligence, sensing data, and associate memory. Neuromorphic chips consume less energy and deliver superior performance than conventional CPU chips owing to their different designs.
Moreover, driving the AI market requires hardware accelerators both for in-production AI applications and for the research and development community that is still working out the underlying simulators, algorithms, and circuitry optimization tasks needed to drive advances in the cognitive computing where all higher-level applications rely on. Neuromorphic hardware doesn’t intend to replace GPUs, CPUs, ASICs, and other AI-accelerator chip architectures, instead, they supplement other hardware platforms so that each can process the specialized AI workloads for which they were designed.
The trait of many neuromorphic architectures, including IBM’s, is asynchronous spiking neural networks (SNNs), which are artificial neural networks that more closely mimic natural neural networks. Intel, for instance, has also been a pioneer in the neuromorphic hardware segment. The company’s Loihi is a self-learning neuromorphic chip for training and inferencing workloads at the edge and also in the cloud. It is designed to expedite parallel computations that are self-optimizing, event-driven, and fine-grained. The core of Loihi’s intelligence is a programmable microcode engine for on-chip training of models that integrate asynchronous SNNs. Each Loihi chip is highly power-efficient and scalable, contains over 2 billion transistors, 130,000 artificial neurons, and 130 million synapses, along with three cores that specialize in orchestrating firings across neurons.