Reimagining storage for the generative AI era
Research
Talia Gershon, Mike Murphy, Swaminathan Sundararaman, Haris Pozidis, and Khanh Ngo
AI Hardware
The world is generating reams of data each day, and the AI systems built to make sense of it all constantly need faster and more robust hardware. We’re developing new devices and architectures to support the tremendous processing power AI requires to realize its full potential.
Our work
How IBM Research helped prepare the IBM z17 for tomorrow’s workloads
NewsPeter HessWhy a decades old architecture decision is impeding the power of AI computing
ExplainerPeter HessHow analog in-memory computing could power the AI models of tomorrow
ResearchPeter HessHow John Knickerbocker spent a career cultivating new technologies
Deep DivePeter HessHow low-precision computing boosts efficiency — without hurting accuracy
ExplainerPeter Hess- See more of our work on AI Hardware
Analog AI: A New Design Paradigm
At IBM Research we’re developing a new class of Analog AI hardware, purpose built to help innovators realize the promise of the next stages of AI. Journey inside this unique architecture.
Publications
DiReC: Enhancing VHDL Code Generation and Summarization with Divide-Retrieve-Conquer Strategy
- Prashanth Vijayaraghavan
- Apoorva Nitsure
- et al.
- 2025
- DAC 2025
Routing Congestion Mitigation Techniques Targeting Dense Designs
- Alex Suess
- Lakshmi Reddy
- et al.
- 2025
- DAC 2025
Architecture and Design Approaches towards Large-scale AI Hardware Acceleration
- Ashish Ranjan
- 2025
- DAC 2025
A Near-DRAM Accelerator for Compiler-Generated Fully Homomorphic Encryption Applications
- 2025
- ISCA 2025
On-Chip Training and Inference using Analog CMO/HfOx ReRAM Artificial Synapses
- Donato Francesco Falcone
- Victoria Clerico
- et al.
- 2025
- Neuronics 2025
Deep Neural Network Inference with Analog In-Memory Computing
- 2025
- PASC 2025
AI Hardware Center
The IBM Research AI Hardware Center is a global research collaboration hub dedicated to creating the next generation of systems and chips for AI workloads, as well as expanding joint research efforts across technology, architecture, and algorithms.
Explore the center