以下文章来源于芯东西 ，作者ZeR0 芯东西 . 芯东西专注报道芯片、半导体产业创新，尤其是以芯片设计创新引领的计算新革命和国产替代浪潮；我们是一群追"芯"人，带你一起遨游"芯"辰大海。 来源 | 芯东西 他也曾是ATI R300和R600系列的首席架构师。当年R300创造过辉煌历史，搭载它的Radeon 9700和9500系列显卡曾对当时英伟达的产品 构成强有力的挑战。AMD收购ATI后，德默斯成为AMD图形部门的首席技术官，然后在2012年加入高通。 业界普遍认为，德默斯的加入会令英特尔GPU团队实力大增。 芯东西2月4日消息， 据外媒报道，英特尔CEO陈立武周二透露，英特尔计划生产GPU。"我刚刚聘请了首席GPU架构师，他非常优秀。我很 高兴他能加入我的团队。"陈立武说，并称自己费了一番功夫才说服他。 此前在今年1月，在高通工作长达14年的资深GPU硬件架构师 埃里克·德默斯（Eric Demmers） 宣布加入英特尔。他在领英网发表的一篇文 章中写道，过去几个月，他多次与陈立武交谈和会面，对陈立武的信心和乐观态度印象深刻，也对在新英特尔工作并参与这一持续转型感到兴 奋。 德默斯曾担任高通工程高级副总裁 ...

Core Insights - The Snapdragon 8 Elite Gen 5 is introduced as the fastest mobile system-on-a-chip (SoC) ever built, featuring the 3rd Generation Qualcomm Oryon CPU, a new Adreno GPU architecture, and an upgraded Hexagon NPU, with significant performance improvements: CPU performance up by 20%, GPU rendering improved by 23%, and AI processing speed increased by 37% [1][5]. Performance Enhancements - The new platform enhances everyday smartphone experiences, focusing on lightning-fast multitasking, seamless app switching, and improved efficiency for long gaming sessions [1][5]. AI Capabilities - The Snapdragon 8 Elite Gen 5 supports advanced on-device AI, enabling "agentic AI" that learns from user behavior, processes information in real time, and acts proactively across applications, ensuring personal data remains on the device [5]. - It introduces the ability to record in the Advanced Professional Video (APV) codec, combined with AI-powered imaging tools, aimed at providing creators with more control over video production from capture to post-production [2][5]. Industry Rollout - The platform will debut in flagship devices from various manufacturers, including Samsung, Xiaomi, OnePlus, OPPO, vivo, Honor, iQOO, Nubia, POCO, realme, REDMI, RedMagic, ROG, Sony, and ZTE, with launches expected soon [3][5].

Core Viewpoint - The article introduces the embedded GPU (e-GPU), a configurable RISC-V GPU platform designed specifically for ultra-low-power edge devices (TinyAI), addressing the challenges of power consumption and area constraints in traditional GPU implementations [1][6]. Group 1: Introduction and Background - The increasing demand for real-time computing driven by machine learning is propelling the rapid development of edge computing, which enhances privacy and energy efficiency by processing data locally rather than relying on cloud servers [4]. - Specialized hardware architectures are required to meet the performance, real-time response, and power consumption limitations of these workloads, with heterogeneous architectures integrating CPUs and domain-specific accelerators being an effective solution [4][5]. - Traditional GPUs have not been thoroughly studied for their trade-offs in ultra-low-power edge devices, which typically operate under strict power constraints in the tens of milliwatts range [5][6]. Group 2: e-GPU Architecture and Features - The e-GPU architecture is designed to minimize area and power consumption while being adaptable to TinyAI applications, featuring a configurable design that allows for optimization of area and power [24][25]. - The memory hierarchy employs a unified architecture that maps the host's main memory and e-GPU global memory to the same physical memory, enhancing programmability and reducing data transfer complexity [26][27]. - A dedicated controller manages e-GPU operations, integrating power management features to monitor and control the power state of computation units [29]. Group 3: Performance Evaluation - The e-GPU configurations were tested using two benchmark tests: General Matrix Multiplication (GeMM) and TinyBio, demonstrating significant performance improvements and energy savings [48][49]. - The e-GPU system achieved speedups of up to 15.1 times and energy reductions of up to 3.1 times compared to baseline systems, while maintaining a power budget of 28 mW [2][58]. - The area of the e-GPU system ranged from 0.24 mm² to 0.38 mm², proving its feasibility for deployment in TinyAI applications, which typically have strict area constraints [50]. Group 4: Industry Context - Commercial edge GPU solutions, such as Qualcomm's Adreno and ARM's Mali GPUs, are not specifically designed for TinyAI applications, often exceeding the power requirements needed for these applications [11]. - Academic GPU research focuses on developing programmable and configurable architectures suitable for various computing domains, with the e-GPU proposed as a suitable solution for TinyAI workloads [12][13]. - The e-GPU platform is positioned as an open-source, configurable RISC-V GPU platform that addresses the programming limitations and energy efficiency needs of the TinyAI domain [12][13].