Whileone Tech Space

Modern compute platforms - from cloud hyperscale CPUs to edge processors - deliver unprecedented parallelism and instruction-set capabilities. But to truly understand performance, you need the right benchmarking tools. Whether you're comparing cloud instances, evaluating Arm-based servers like Ampere , or validating x86, RISC-V, or AI-accelerated hardware, the ecosystem offers several battle-tested frameworks. In this blog, we explore the most widely-used CPU benchmarking too

Inspiration In high-throughput production environments, every sensor reading tells a story. Test systems continuously record Pressure , Temperature , and Differential Loss (DL)  across thousands of cycles, but much of this data remains passive, observed but not interpreted. We set out to change that by deploying machine learning directly at the edge  on a BeagleBone Black  board. The goal was not anomaly detection, but live inference : to compute what the ideal DL should be (

Goal of this project: This project aims to determine RISC-V's readiness for running YOLOX for the latest edge requirements. Target Application: Running YOLOX on RISC-V QEMU involves setting up a RISC-V virtual machine and then configuring the necessary environment to compile and run YOLOX. Please note that this is a complex process, and it's essential to have prior experience with virtualization and RISC-V development. From the RISCV website, this is a blog ( https://riscv.or