Hardware-aware AI Optimization

Modern AI algorithms often demand significant computational resources for training and deployment. The trend towards larger neural networks still persists today, posing challenges for running complex algorithms swiftly (in real-time) and with optimal power efficiency. This becomes crucial in environments such as fast production lines where rapid algorithm execution is essential for timely and accurate quality inspection and logging.

Ceyeborg addresses these challenges through hardware-AI co-design methods, ensuring your AI algorithms perform efficiently on your chosen hardware platform without compromising their output quality.

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Hardware-aware AI Optimization

Hardware-AI co-design is a methodology that integrates both hardware and software considerations when planning AI deployments. AI algorithms can be optimized during the design, training, or post-training phases to align with the target hardware. For example, inefficient mathematical operations can be replaced with faster alternatives (e.g., Winograd convolution vs. standard convolution), and layer dimensions can be adjusted to better fit the hardware’s memory, reducing costly off-chip DRAM accesses. Ceyeborg’s experts have extensive experience in hardware-AI co-design, with numerous novel techniques developed and published in the scientific community.

AI compression techniques are employed to streamline networks, enhancing execution times, power efficiency, and memory footprint. Ceyeborg’s experts utilize techniques such as neural architecture search, pruning, and quantization to create optimized networks for specific tasks. Neural architecture search explores network structures that can efficiently learn perception tasks. Pruning eliminates low-saliency elements (e.g., neurons, synapses) to reduce the network’s size and computational load. Quantization lowers the numerical precision of network parameters (e.g., from Float32 to 8-bit, 4-bit, or even 1-bit), simplifying hardware execution and decreasing the network’s memory footprint.

These measures ensure that Ceyeborg’s AI systems never miss any anomalies, events, or defects, even on the most demanding production line speeds and deployment environments.

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