At VLSI 2020, IBM Research is spotlighting key developments for hybrid cloud infrastructure and AI, marked by improvements in performance, energy efficiency, area scaling, and new workloads.
Scientists around the world are inspired by the brain and strive to mimic its abilities in the development of technology. Our research team at IBM Research Europe in Zurich shares this fascination and took inspiration from the cerebral attributes of neuronal circuits like hyperdimensionality to create a novel in-memory hyperdimensional computing system. The most […]
Can analog AI hardware support deep learning inference without compromising accuracy? Our research team at IBM Research Europe in Zurich thought so when we started developing a groundbreaking technique that achieves both energy efficiency and high accuracy on deep neural network computations using phase-change memory devices. We believe this could be a way forward in […]
At NeurIPS 2019, IBM Research continues to advance its 8-bit training platform to improve performance and maintain accuracy for the most challenging emerging deep learning models.
Researchers from the IBM AI Hardware Center will showcase at IEDM and NeurIPS new analog devices, algorithmic and architectural solutions, a novel model training technique, and a full custom design.
At IEDM, the top conference for semiconductor device technology, IBM Research presents the latest progress in nanosheet technology, including new critical features for high performance computing.
After uncovering a new Nasca Line formation with IBM Watson Machine Learning Accelerator on IBM Power Systems, Yamagata University will deploy IBM PAIRS in the hopes of further discoveries with AI.
The fourth-quarter issue of the IBM Journal of Research & Development is dedicated to the exploration and deployment of hardware for AI systems. It contains 10 contributions from leading authorities in the fields that summarize the latest state of the art and share new research results.
At the 2019 VLSI, IBM researchers will present three papers that provide novel solutions to AI computing based on analog devices.
IBM researchers introduce accumulation bit-width scaling, addressing a critical need in ultra-low-precision hardware for training deep neural networks.
IBM Research shares new results at SysML that push the envelope for deep learning inference, enabling high accuracy down to 2-bit precision.
The IBM Research AI Hardware Center, a global research hub to develop next-generation AI hardware and help achieve AI's true potential.