AI ASIC Landscape: Google TPU Evolution, Intel Habana Gaudi 2, IBM AIU, and Samsung Warboy NPU

Leading semiconductor companies are increasingly investing in AI ASICs, each following distinct technical paths. Google launched the first TPU (ASIC) in 2015 and has continuously upgraded the line, reaching TPU v4 in 2021 with a 7 nm process and a peak performance of 275 TFLOPS.

Intel acquired Israel‑based Habana Labs in 2019 and released the Gaudi 2 ASIC in 2022. Gaudi 2 features dual compute engines (MME and TPC), RDMA‑based interconnect, and delivers training throughput on ResNet‑50, BERT and related models that surpass Nvidia A100.

IBM’s research institute announced the AIU ASIC for a 2023 launch, while Samsung has begun mass production of its first‑generation AI ASIC, the Warboy NPU.

ASICs offer high performance, small footprint, and low power consumption compared with CPUs, GPUs, and FPGAs, making them especially advantageous for AI inference where they can be 100‑1000× more efficient than CPUs.

Market forecasts (CSET, McKinsey) predict ASICs will capture 40‑70 % of AI workloads in data‑center and edge environments by 2025, with a particularly strong presence in inference.

The article also outlines the architectural details of Google’s TPU family: TPU v1’s unified buffer and matrix‑multiply unit occupy 53 % of die area; TPU v2 doubles the number of Tensor Cores; TPU v3 adds liquid‑cooling and further MXU scaling; TPU v4 introduces optical interconnects and reconfigurable optical switches to improve bandwidth and fault tolerance.

Intel’s Gaudi architecture implements parallel MME and TPC engines, expands TPC count from 8 to 24 in Gaudi 2, increases HBM capacity to 96 GB, and integrates RDMA for high‑speed chip‑to‑chip communication, enabling efficient AI cluster scaling.