The article reviews the historical shift from CPU‑based ECUs to GPU‑centric and ASIC‑centric automotive processors, analyzes current GPU dominance, examines key industry players, and discusses why ASICs are expected to become the primary solution for future autonomous‑driving chips.
The article analyzes RISC‑V’s open, modular ISA, its technical advantages over legacy architectures, the rapidly maturing global and Chinese ecosystems, real‑world applications, and strategic recommendations for China to build an independent, competitive semiconductor industry amid trade tensions and policy drives.
This article summarizes recent developments across data‑center, cloud, 5G, edge, AI, security, automotive and aerospace sectors, including new partnerships, product launches, government contracts, and emerging threats shaping the pervasive computing landscape.
This week’s tech roundup highlights JD’s new federated learning platform, Facebook’s AI-driven search for renewable-energy catalysts, ARM’s high‑performance AIPU, NVIDIA’s data‑center DPU, Chrome’s rollout of HTTP/3 with QUIC, Canonical’s take on Windows‑Linux migration, plus recent advances in stereo matching and mobile sensor action recognition.
This article explains how Alibaba Cloud builds a high‑performance AI infrastructure by combining advanced hardware such as Shenlong servers, GPUs, FPGAs, NPUs, and custom interconnects like RDMA, together with virtualization, FPGA‑as‑a‑Service, AIACC, and resource‑pooling technologies to deliver scalable, cost‑effective AI services.
The article examines the growing bottlenecks of traditional compute architectures, explains why breaking the storage wall through high‑bandwidth communication, near‑data processing, and in‑memory compute is essential for AI workloads, and surveys the principles, advantages, challenges, future directions, and key industry players of integrated compute‑storage chips.
This article compares CPU, GPU, FPGA, TPU, and ASIC based AI servers on performance and programmability, explains selection factors such as power, cost, precision, and memory, and provides practical guidelines for choosing appropriate GPU server architectures and models.
Deep neural networks, propelled by breakthroughs such as AlexNet and advances in GPU and TPU hardware, are rapidly moving from academic research into diverse applications—including earthquake prediction, medical imaging, and autonomous driving—driving massive industry investment, new semiconductor designs, and intense competition among tech giants and startups.
Prof. Yin Shouyi of Tsinghua University presented a reconfigurable, low‑bit quantized neural‑network architecture and the Thinker‑I, Thinker‑II, and Thinker‑S chips, demonstrating ultra‑low power consumption and high energy‑efficiency for AI deployment on edge devices.
