Today, in advance of ISC High Performance 2024, SpiNNcloud Systems announced the commercial availability of its SpiNNaker2 platform, a supercomputer-level hybrid AI high-performance computer system based on principles of the human brain. Pioneered by Steve Furber, designer of the original ARM and SpiNNaker1 architectures, the SpiNNaker2 supercomputing platform uses a large number of low-power processors for efficiently computing AI and other workloads.

First-generation SpiNNaker1 architecture is currently used in dozens of research groups across 23 countries worldwide. Sandia National Laboratories, Technical University of München and Universität Göttingen are among the first customers placing orders for SpiNNaker2, which was developed around commercialized IP invented in the Human Brain Project, a billion-euro research project funded by the European Union to design intelligent, efficient artificial systems.

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

On Day 2 of Intel Innovation, Intel illustrated how its efforts and investments to foster an open ecosystem catalyze community innovation, from silicon to systems to apps and across all levels of the software stack. Through an expanding array of platforms, tools and solutions, Intel is focused on helping developers become more productive and more capable of realizing their potential for positive social good. The company introduced new tools to support developers in artificial intelligence, security and quantum computing, and announced the first customers of its new Project Amber attestation service.

"We are making good on our software-first strategy by empowering an open ecosystem that will enable us to collectively and continuously innovate," said Intel Chief Technology Officer Greg Lavender. "We are committed members of the developer community and our breadth and depth of hardware and software assets facilitate the scaling of opportunities for all through co-innovation and collaboration."

Intel Labs, in collaboration with the Italian Institute of Technology and the Technical University of Munich, has introduced a new approach to neural network-based object learning. It specifically targets future applications like robotic assistants that interact with unconstrained environments, including in logistics, healthcare or elderly care. This research is a crucial step in improving the capabilities of future assistive or manufacturing robots. It uses neuromorphic computing through new interactive online object learning methods to enable robots to learn new objects after deployment.

Using these new models, Intel and its collaborators successfully demonstrated continual interactive learning on Intel's neuromorphic research chip, Loihi, measuring up to 175x lower energy to learn a new object instance with similar or better speed and accuracy compared to conventional methods running on a central processing unit (CPU). To accomplish this, researchers implemented a spiking neural network architecture on Loihi that localized learning to a single layer of plastic synapses and accounted for different object views by recruiting new neurons on demand. This enabled the learning process to unfold autonomously while interacting with the user.

Polyn Technology announced today that its first Neuromorphic Analog Signal Processor (NASP) chip is packaged and evaluated, demonstrating proof of the technology's brain-mimicking architecture. It is the first Tiny AI true analog design to be used next to sensors. Polyn Technology is an innovative provider of ultra-low-power-performance NASP technology and a producer of unique Tiny AI chips and their associated IP. "This achievement validates the intensive work of our multinational team," said Aleksandr Timofeev, CEO and founder of Polyn Technology. "Our chip represents the most advanced technology bridging analog computations and the digital core. It is designed with neuroscience in mind, replicating pre-processing the primary cortical area of the human brain does at the periphery before learning at the center."

The NASP chip enables full data processing disaggregation between the sensor node and the cloud; it truly embodies the Tiny AI concept. The NASP test chip contains several neural networks. The chip is implemented in 55 nm CMOS technology. Its design proves the NASP "neuron" model as well as the scalability of the technology and efficiency of the chip design automation tools developed by Polyn. "Our first chip is created from trained neural networks by NASP Compiler and synthesis tools that generated Netlist and the silicon engineering files from the software math model simulation. We will continue to refine our technology for creation of new generation chips," said Yaakov Milstain, COO of Polyn. Polyn anticipates the chip will be available to customers in the first quarter of 2023 as its first wearables product, with a fusion of PPG and IMU sensors for the most accurate heart rate measurement along with recognition and tracking of human activity.

Today, Intel Federal LLC announced a three-year agreement with Sandia National Laboratories (Sandia) to explore the value of neuromorphic computing for scaled-up computational problems. Sandia will kick off its research using a 50-million neuron Loihi-based system that was delivered to its facility in Albuquerque, New Mexico. This work with Loihi will lay the foundation for the later phase of the collaboration, which is expected to include continued large-scale neuromorphic research on Intel's upcoming next-generation neuromorphic architecture and the delivery of Intel's largest neuromorphic research system to date, which could exceed more than 1 billion neurons in computational capacity.

"By applying the high-speed, high-efficiency and adaptive capabilities of neuromorphic computing architecture, Sandia National Labs will explore the acceleration of high-demand and frequently evolving workloads that are increasingly important for our national security. We look forward to a productive collaboration leading to the next generation of neuromorphic tools, algorithms, and systems that can scale to the billion neuron level and beyond," said Mike Davies, director of Intel's Neuromorphic Computing Lab.

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.