In the quest for more efficient AI, we're witnessing a renaissance of analog computing. At IBM Research, our work on analog in-memory computing is demonstrating that the future of AI hardware may look very different from today's digital processors.
Why Analog?
Digital computing excels at precision but struggles with the massive parallelism required for neural network inference. Analog computing, by performing computations directly in memory using physical properties of materials, can achieve orders-of-magnitude improvements in energy efficiency.
The IBM Analog Hardware Acceleration Kit
Our open-source toolkit, AIHWKIT, enables researchers worldwide to explore analog AI. It provides simulation tools, training algorithms, and hardware-aware optimization techniques that bridge the gap between analog hardware capabilities and AI model requirements.
Neural Architecture Search for Analog
One of our most exciting research directions is using neural architecture search to automatically discover network architectures that are optimized for analog hardware. This co-design approach ensures that AI models and analog hardware evolve together, maximizing the benefits of both.
The analog computing renaissance is not about replacing digital computing — it's about finding the right tool for the right job. For AI inference, analog may well be that tool.
Dr. Kaoutar El Maghraoui
Principal Research Scientist at IBM Research · Adjunct Professor at Columbia University