➀ This article discusses the research on accelerating RTL simulation with hardware-software co-design; ➁ It compares the performance of Chronos and SASH, the latest advancements in hardware acceleration for RTL simulation; ➂ The article explores the potential impact of these technologies on the EDA industry.
Recent #Hardware Acceleration news in the semiconductor industry
➀ The impact of AI on software and hardware development; ➁ The evolution of software from 'eating the world' to 'consuming hardware'; ➂ The comparison between traditional software and AI applications in terms of processing hardware; ➃ The two-stage development process of AI software and its validation challenges.
➀ Systolic arrays face challenges in AI inference due to their structured design; ➁ Arteris NoCs enable flexibility in accelerator arrays; ➂ Innovations like CGRA and dynamic frequency scaling are driving progress in AI hardware.
➀ The DVCon Europe 2024 conference highlighted the rise of AI and software in design and verification. ➁ Keynotes from Infineon and Zyphra discussed AI microcontroller architectures and datacenter-scale AI systems. ➃ Software-defined vehicles and open-source technologies like RISC-V were also key topics.
➀ NPU stands for Neural Processing Unit and acts as a hardware accelerator for AI.
➁ NPU complements CPU and GPU, handling tasks like edge AI.
➂ NPU is designed for high throughput and parallel workloads, such as neural networks and machine learning.
➃ NPU is increasingly used in consumer devices like laptops and PCs, as well as in cloud-based systems.
➄ The rise of NPU is driven by the importance of edge intelligence and the need for local data processing.
➀ Intel's recent adjustments in storage technology, including the sale of NAND SSD business and discontinuation of Optane development; ➁ Introduction of advanced microarchitecture, more cores, and enhanced memory bandwidth in Intel Xeon 6 processors to improve storage capabilities; ➂ NTB technology for communication and data synchronization between high-performance storage nodes, enhancing system stability and security; ➃ CXL 2.0 technology for memory pooling and resource sharing between storage nodes; ➄ Hardware accelerators like DSA and QAT for data migration, processing, compression, and encryption, improving system performance; ➅ VMD and VROC technologies for ensuring data reliability and security; ➆ Software innovations like ISA-L and SPDK for enhancing data processing and reliability; ➇ Contributions to open ecosystems and support for open-source projects like Ceph and DAOS; ➈ Comprehensive support for hyper-converged storage architectures with Intel Xeon processors, storage solutions, and network technologies.