Uniqomp provides unary‑computing FPGA IP cores that replace binary data representation with an uncompressed unary format to deliver higher throughput, lower power consumption, and reduced silicon area. The cores accept binary inputs, perform unary‑based arithmetic, and output binary results, allowing hardware designers in 5G, machine‑learning, video streaming, and high‑frequency trading to integrate the technology as a drop‑in black box within existing design flows.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional binary digital computing often requires large silicon area, high power consumption, and limited throughput for compute‑intensive workloads such as 5G signal processing, machine‑learning inference, video encoding, and high‑frequency trading. These constraints increase hardware cost and operational expenses for designers of modern, performance‑critical applications.
Solution
Uniqomp offers a unary‑computing methodology that replaces binary data representation with an uncompressed unary format to execute arithmetic operations more efficiently. By providing FPGA IP cores that accept binary inputs, perform unary‑based computation, and return binary results, the company enables designers to integrate the technology as a drop‑in black box within existing digital design flows. Experimental results show higher throughput, reduced chip area, and lower power draw compared with conventional binary implementations, while remaining compatible with standard design tools and synthesis pipelines. Uniqomp supports a range of application domains, delivering ready‑to‑use IP for 5G base‑band processing, machine‑learning accelerators, video streaming pipelines, and high‑frequency trading engines.
Target Audience
Primary customers are FPGA‑based hardware designers and system integrators in telecommunications, artificial‑intelligence accelerators, video processing, and financial‑technology firms that require high‑performance, low‑power compute blocks.
Features
- Unary‑computing FPGA IP cores that convert binary inputs to unary, execute operations, and reconvert results to binary
- Compatibility with standard HDL design tools and synthesis flows, allowing seamless integration into existing projects
- Demonstrated reductions in silicon area, power consumption, and latency across benchmarked workloads
- Application‑specific optimized cores for 5G signal processing, neural‑network inference, real‑time video encoding, and low‑latency trading algorithms
- Parameterizable architecture enabling designers to trade off precision, throughput, and resource usage per use case