AI Accelerator Inference

AI accelerator inference concerns how to run trained models on GPUs, TPUs, NPUs, and custom chips with low latency and energy use while preserving output quality. Common approaches include quantization and mixed precision, fused kernels, memory and cache optimization, workload scheduling, and co-designing model algorithms with accelerator dataflows. Research spans large language models and mixture-of-experts serving as well as vision, speech, and embodied AI, often measuring prefill and decoding separately or evaluating real-time edge workloads. A central challenge is that performance depends strongly on the model, inference phase, hardware, and software stack, so gains on one platform may not transfer and can come with accuracy, energy, or portability costs.