Overview
The SiFive U74 Standard Core is a single-core instantiation of the world's highest performance RISC-V application processor, capable of supporting full-featured operating systems such as Linux.
The U74 is ideal for applications requiring high-throughput, single-thread performance -- but have power constraints (e.g., AR, VR, sensor hubs, IVI systems, IP cameras, digital cameras, gaming devices, etc.)
Learn more about CPU IP core
For the first time in our more than 35-year history, Arm is delivering its own silicon products – extending the Arm Neoverse platform beyond IP and Arm Compute Subsystems (CSS) to give customers greater choice in how they deploy Arm compute – from building custom silicon to integrating platform-level solutions or deploying Arm-designed processors.
The ChiPy DSL is Quadric's Python framework for building complete on-chip pipelines. Using YOLOX-M as a case study, we show how backbone inference, box decoding, and NMS run entirely on the Chimera GPNPU — no host CPU intervention, no DDR round-trips, just Python compiled to silicon.
As part of the new Arm Lumex compute subsystem (CSS) platform, the Arm C1 CPU cluster – the first built on the Armv9.3 architecture – is the next evolution of our highest performing CPU cluster for consumer devices, designed to unleash the full potential of on-device AI and elevate the user experience.
Hardware fuzzing has recently gained momentum with many discovered bugs in open-source RISC-V CPU designs. Comparing the effectiveness of different hardware fuzzers, however, remains a challenge: each fuzzer optimizes for a different metric and is demonstrated on different CPU designs.
Unlock ultra-efficient performance, advanced AI processing, and robust security with the Cortex-A320—designed to power the future of IoT and edge AI innovation.
Pie maintains low computation latency, high throughput, and high elasticity. Our experimental evaluation demonstrates that Pie achieves optimal swapping policy during cache warmup and effectively balances increased memory capacity with negligible impact on computation. With its extended capacity, Pie outperforms vLLM by up to 1.9X in throughput and 2X in latency. Additionally, Pie can reduce GPU memory usage by up to 1.67X while maintaining the same performance. Compared to FlexGen, an offline profiling-based swapping solution, Pie achieves magnitudes lower latency and 9.4X higher throughput.
