Summary
Running artificial intelligence workloads requires vast amounts of memory and energy, motivating a new generation of memory and transistor technologies that pack more storage closer to compute and operate at lower power. These emerging devices are promising candidates for accelerating AI, but they experience data corruption at rates far higher than conventional hardware, making them unreliable without costly error-correction techniques that erase their efficiency advantage. This project develops software tools and mathematical foundations that allow AI algorithms to be written so that they are
