Whileone Tech Space

Benchmarking SPECint on FPGA: Introductio n With the growing interest in AI hardware for high-performance and power-efficient computing, understanding how industry-standard benchmarks perform on such platforms is critical. In this paper, we focus on SPECrate®2017 Integer workloads, a widely-used CPU benchmark suite, and share a case study comparing various runs on an FPGA target: a base run and a tuned run that achieved better performance. This paper describes how the tuning

LLMs are Getting Better and Smaller Let’s look at Llama as an example. The rapid evolution of these models highlights a key trend in AI: prioritizing efficiency and performance. When Llama 2 70B launched in August 2023, it was considered a top-tier foundational model. However, its massive size demanded powerful hardware like the NVIDIA H100 accelerator. Less than nine months later, Meta introduced Llama 3 8B, shrinking the model by almost 9x. This enabled it to run on smaller

SPEC CPU2017 is a well-known benchmark suite for evaluating CPU-intensive performance. Although it assumes native compilation and execution, there are cases—especially with RISC-V (RV64) platforms—where cross-compilation is the only feasible route. This guide walks through the steps to cross-compile SPEC CPU2017 for RISC-V, transfer the binaries to a target system, and optionally use the --fake option to simulate runs where execution isn't possible or needed during develop

One way to scale a Node based system is to run multiple instances of the server. This approach also works well for Strapi because Strapi doesn't store anything in-memory on the server-side (no sticky sessions). The JWT tokens it issues persist in the database. So, any time we observe a Strapi setup struggling to handle the load of requests on the Node side, we add more instances running the same code. For setups with a predictable workload, pm2 offers a simple way to manage m