What Are the Core Metrics Behind AI Chips? A Deep Dive into GPU, ASIC, and TPU
This article explains the fundamental performance indicators of AI chips—TOPS, TFLOPS, and precision formats like FP16, FP32, and INT8—while comparing GPU, ASIC, and TPU architectures, highlighting Tensor Core advantages and TPU's superior efficiency over CPUs and GPUs.
AI compute capability, measured in TOPS and TFLOPS, is provided by specialized chips such as GPUs, ASICs, and FPGAs for model training and inference. Precision formats like FP16 and FP32 are used for training, while FP16 and INT8 serve inference.
GPU‑based AI chips typically combine GPU and ASIC architectures. The GPU’s compute power and memory bandwidth determine its performance. Its core units include Cuda Cores and Tensor Cores. Tensor Cores are optimized for deep‑learning matrix operations, delivering up to 12× higher AI throughput than comparable Cuda Cores (e.g., Nvidia Volta vs. Pascal).
TPU, Google’s ASIC for machine learning, outperforms CPUs and GPUs in energy‑efficiency. TPU v1 can achieve up to 71× the neural‑network performance of contemporary CPUs and 2.7× that of GPUs.
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