Today, Intel announced that it has built the world's largest neuromorphic system. Code-named Hala Point, this large-scale neuromorphic system, initially deployed at Sandia National Laboratories, utilizes Intel's Loihi 2 processor, aims at supporting research for future brain-inspired artificial intelligence (AI), and tackles challenges related to the efficiency and sustainability of today's AI. Hala Point advances Intel's first-generation large-scale research system, Pohoiki Springs, with architectural improvements to achieve over 10 times more neuron capacity and up to 12 times higher performance.

"The computing cost of today's AI models is rising at unsustainable rates. The industry needs fundamentally new approaches capable of scaling. For that reason, we developed Hala Point, which combines deep learning efficiency with novel brain-inspired learning and optimization capabilities. We hope that research with Hala Point will advance the efficiency and adaptability of large-scale AI technology." -Mike Davies, director of the Neuromorphic Computing Lab at Intel Labs

Today, Intel introduced Loihi 2, its second-generation neuromorphic research chip, and Lava, an open-source software framework for developing neuro-inspired applications. Their introduction signals Intel's ongoing progress in advancing neuromorphic technology. Neuromorphic computing, which draws insights from neuroscience to create chips that function more like the biological brain, aspires to deliver orders of magnitude improvements in energy efficiency, speed of computation and efficiency of learning across a range of edge applications: from vision, voice and gesture recognition to search retrieval, robotics, and constrained optimization problems.

"Loihi 2 and Lava harvest insights from several years of collaborative research using Loihi. Our second-generation chip greatly improves the speed, programmability, and capacity of neuromorphic processing, broadening its usages in power and latency constrained intelligent computing applications. We are open sourcing Lava to address the need for software convergence, benchmarking, and cross-platform collaboration in the field, and to accelerate our progress toward commercial viability." -- Mike Davies, director of Intel's

Today, Intel announced the readiness of Pohoiki Springs, its latest and most powerful neuromorphic research system providing the computational capacity of 100 million neurons. The cloud-based system will be made available to members of the Intel Neuromorphic Research Community (INRC), extending their neuromorphic work to solve larger, more complex problems.

"Pohoiki Springs scales up our Loihi neuromorphic research chip by more than 750 times, while operating at a power level of under 500 watts. The system enables our research partners to explore ways to accelerate workloads that run slowly today on conventional architectures, including high-performance computing (HPC) systems." -Mike Davies, director of Intel's Neuromorphic Computing Lab.

Intel has been steadily increasing its portfolio of products in the AI space, through the acquisition of multiple AI-focused companies such as Nervana, Mobileye, and others. Through its increased portfolio of AI-related IP, the company is looking to carve itself a slice of the AI computing market, and this sometimes means thinking inside the box more than outside of it. It really doesn't matter the amount of cores and threads you can put on your HEDT system: the human brain's wetware is still one of the most impressive computation machines known to man.

That idea is what's behind of neuromorphic computing, where chips are being designed to mimic the overall architecture of the human brain, with neurons, synapses and all. It marries the fields of biology, physics, mathematics, computer science, and electronic engineering to design artificial neural systems, mimicking the morphology of individual neurons, circuits, applications, and overall architectures. This, in turn, affects how information is represented, influences robustness to damage due to the distribution of workload through a "many cores" design, incorporates learning and development, adapts to local change (plasticity), and facilitates evolutionary change.