The world is analog. Intelligence, today, is digital. Someone has to build the bridge.

Every voice a microphone hears, every photon a camera collects, every bit racing between two GPUs training a language model begins or ends as a continuous physical signal. Our laboratory designs the CMOS circuits that live on that boundary: mixed-signal circuits that turn the physical world into numbers, and numbers back into the physical world, at ever higher speed and ever lower energy.

Why now

Look at the two chips on the left. What makes them one machine is not the silicon inside each package but the dense bundle of high-speed lanes between them, and those lanes are where the AI era has moved the bottleneck. The chart on the right tells the story in numbers. The interconnect bandwidth of a single GPU grew from 0.16 TB/s in 2016 to 1.8 TB/s in 2024, more than tenfold in eight years. But every bit that crosses a wire costs energy. At a few picojoules per bit, the dashed curves show that simply moving data in and out of one accelerator now demands tens of watts, a power budget that rivals the computation it serves and that scales with every new generation.

That energy is spent in exactly three kinds of circuits: the transceivers that drive and receive the lanes, the clocks that keep both ends of every lane in step, and the converters that let the digital world sense and reach the analog one. The frontier of computing is no longer only inside the processor; it is in the links between processors, in the timing that binds them, and in the converters at their edges. That is where our work sits.