Neurealm customized, benchmarked, and deployed a complex deep neural network on an AI hardware accelerator, validating performance on an emulator toolbox and an FPGA prototype for a U.S. semiconductor customer.

Business Context

A U.S.-based semiconductor company developing an AI SoC for object detection across 80+ classes needed expert support to optimize and port a custom neural network onto constrained accelerator hardware. Challenges included managing low-memory deployment, handling unsupported network layers, and aligning engineering execution with business goals to demonstrate product viability early in design.

Solutions

Neurealm engineered preprocessing and postprocessing code in C, adapted the custom network for target hardware, and devised effective workarounds for unsupported layers. They ran real-world applications on the emulator and FPGA, validated functional performance, and benchmarked accuracy close to state-of-the-art networks — delivering early confidence in the AI SoC’s capabilities.