Understanding Neuromorphic Computing

Simply put, neuromorphic computing refers to a means by which computers are particularly developed to mimic the architecture and functioning of the human brain. Like the Human brain, such computing utilizes neurons(nodes), Digi-synapses, etc. to effectively pass and analyze data, recognize patterns, and generate multi-modal decisions. Unlike traditional computers based on the Von Neumann model proposed in 1945, Neuromorphic computers operate on an architecture that combines memory and processing in the same unit. Due to this, the hardware needed for such computing is much different than the parts you commonly hear being used in day-to-day computers. Memristors, PCMs (Phase Change Memory), etc. are some parts used in creating these special computers.

Recently, Intel launched the world's largest neuromorphic computer code-named 'Hala Point' which implements Intel's Loihi2 chipset. The Loihi series according to Intel's own documentation states, 'specialized for a specific SNN model. Loihi 2 now implements its neuron models with a programmable pipeline in each neuromorphic core to support common arithmetic, comparison, and program control flow instructions.'. An SNN refers to a Spiked Neural Network in which neurons, or spiking nodes, process and hold data like biological neurons. The Hala Point system is powered by 1,152 such Loihi2 chips and can perform 20 quadrillion operations per second. This robust system has multiple applications in the fields of Autonomous Driving, Medical Diagnostics, Weather Forecasting, Financial Predictions, etc. Hala Point's capabilities could enable future real-time continuous learning for A.I.-powered applications and LLMs.

Copyright: Intel Corporation

Talking about LLMs, OpenAI signed a letter of intent to buy NPUs (Neural Processing Units) worth $51 Million from a tech startup named Rain AI. The NPUs from Rain AI are expected to enhance OpenAI’s computational capabilities, providing more efficient and powerful processing for their AI models. This decision is driven by the need to handle the vast computational demands of LLMs, which require vast amounts of data processing and real-time learning capabilities. The NPUs will enable OpenAI to scale its GPT models efficiently, reducing latency and energy consumption while improving performance. An investment of such magnitude highlights the significance of neuromorphic computing in the current landscape of Artificial Intelligence, demonstrating its necessity in advancing AI technologies and maintaining competitive advantages in the field.

By mimicking the brain's architecture, neuromorphic systems can achieve unmatched capability, advancing neural network-based applications like ChatGPT, Photoshops Generative Fill, and autonomous systems. As neuromorphic computing continues to develop, it is set to play a crucial role in the evolution of AI, enabling more sophisticated and responsive applications.