AI accelerator
Historical Development of AI Accelerators
Special-purpose accelerators have a long history in computer systems for tasks such as graphics, sound processing, and digital signal processing. In the 2010s, specialized hardware units were developed specifically for deep learning and artificial intelligence tasks. GPUs, FPGAs, video cards, sound cards, and digital signal processors have been utilized as accelerators, with FPGA-based accelerators being explored in the 1990s for both inference and training. Early attempts at AI accelerators include Intels ETANN 80170NX, Nestor/Intel Ni1000, and the use of digital signal processors in the early 1990s.
Hardware Acceleration for AI Workloads
GPUs have evolved to support deep learning for training and inference, while FPGAs offer flexibility in designing custom hardware for evolving deep learning frameworks. Application-specific integrated circuits (ASICs) provide significant efficiency gains over CPUs, GPUs, and FPGAs for AI tasks. Integrated photonic tensor cores leverage optical technology for high-speed data processing in AI applications. Terminology in the field of AI accelerators varies, with terms like GPU, DLP, and TPU being commonly used.
Optimization and Efficiency in AI Accelerators
CPUs have incorporated wide SIMD units and low-precision data type support for AI workloads in the 2000s. ASIC accelerators employ strategies such as optimized memory usage and lower precision arithmetic for faster computation. Half-precision and bfloat16 floating-point formats are utilized for AI acceleration, while DLPs focus on architecture designs for high efficiency in deep neural networks. Hybrid DLPs, like PIM architectures, bridge computing and memory for efficient DNN inference and training.