Hoursec provides a single-chip AI training and inference platform that accelerates computation by up to 3000× compared to conventional solutions. Its architecture reduces data transfer and energy consumption, enabling real-time processing for edge applications such as autonomous vehicles, FPGA‑based anomaly detection, and high‑frequency trading. The technology supports both inference and on‑chip training, delivering nanosecond‑scale analytics without reliance on cloud resources.
Funding
Funding not disclosed
Founders
Product
Problem
The increasing demand for computational power in AI and data analytics is outpacing the supply, leading to high costs, long processing times, and unsustainable energy consumption, especially for real-time applications and edge computing scenarios. Existing solutions often struggle to balance speed, energy efficiency, and the need for local, private data processing.
Solution
Hoursec.tech offers high-performance computing solutions that drastically reduce computation times for AI training and inference, leveraging a novel Single Chip Inference and Training (SCIT) architecture. By integrating a breakthrough algorithm with innovative hardware code optimized for currently available FPGA technology, Hoursec enables computation speeds up to 3000 times faster than current solutions. This approach facilitates the transition of AI applications from cloud to edge, reducing energy consumption by up to 80% while enhancing real-time data processing and error correction. Hoursec's technology allows users to perform complex computations previously unattainable, unlocking new possibilities in physics discovery and exploration.
Target Audience
Hoursec's primary customers include organizations in autonomous driving/flying robots, and users of FPGA, DSP, and SoC who require high-speed processing with low data and high privacy needs, as well as researchers and businesses involved in Monte Carlo simulations, risk analysis, and high-dimensional data analytics.
Features
- Single Chip Inference and Training (SCIT) architecture for simultaneous AI training and inference.
- Optimized hardware code designed for existing FPGA technology, eliminating the need for proprietary hardware.
- Algorithm integrated with hardware code to accelerate computational speed by up to 3000x.
- Reduced training data requirements by up to 80%, improving efficiency and reducing costs.
- Real-time data processing and error correction capabilities for enhanced accuracy.
- Low energy consumption, promoting sustainable AI practices.
- Edge computing capabilities for local and private data processing.