Reiner Pope explains how the physical limits of logic gates, data movement costs, low‑precision arithmetic scaling, and clock synchronization shape the divergent architectures of GPUs, FPGAs, and emerging carbon‑based AI processors.
The Chinese GPU market, valued at 1.546 trillion CNY in 2024 and projected to grow over 30% annually, is reshaping as domestic firms like Huawei, Wallin, and Moore Thread adopt 7nm chiplet designs to challenge Nvidia's dominance, while grappling with software ecosystem gaps and supply‑chain constraints.
At Next 2026 Google unveiled the 8th‑generation TPU, separating training and inference into two dedicated chips—TPU 8t with 121 ExaFLOPS for massive models and TPU 8i with ultra‑low latency memory—while boosting performance, efficiency, and ecosystem support, signaling a shift toward specialized AI hardware and intensifying competition with Nvidia.
The article reviews ten years of RDMA development, tracing its origins, the rise and pitfalls of RoCEv1/v2, alternative approaches like iWARP and Cisco usNIC, and recent modernizations such as AWS SRD, Google Falcon and UltraEthernet, highlighting why protocol design choices have repeatedly stalled industry progress.
This article provides a detailed, line‑by‑line analysis of a chart comparing FPGA and ASIC across dimensions such as upfront costs, unit cost, time‑to‑market, performance, power consumption, field updates, density, design flow, granularity, verification needs, upgrade paths, and additional features, helping engineers decide which technology best fits their high‑performance AI workloads.
This article traces the development of automotive electronic control units from early CPU‑centric ECUs to centralized domain controllers, examines the rise of GPU‑based AI accelerators for assisted driving, and explains why ASICs are expected to dominate future autonomous‑driving chips, while profiling key industry players and their strategies.
