Hybrid AI Acceleration
For more than half a century, computing progress has been driven by a simple premise: if we make processors faster, smaller, and cheaper, intelligence will follow. That assumption has held remarkably well—from mainframes to microprocessors, from CPUs to GPUs, and from general-purpose machines to highly specialized accelerators. Yet today, we find ourselves at a transition point where scaling alone no longer delivers proportional returns.
This book enters the conversation at precisely the right moment.
What limits modern artificial intelligence is not ambition, nor algorithmic ingenuity, nor even data availability. It is the physics of computation itself—energy dissipation, data movement, and the irreversible nature of most classical logic. These constraints are not bugs to be engineered away; they are fundamental properties of how we compute today.
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