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AI Research
arXiv CS.AI·Jun 29·5 min read
SEADA: An efficient methodology for optimizing mixed-precision DNNs on multi-precision spatial architectures
TL;DR
Deep neural networks (DNNs) often struggle with efficiency when using mixed-precision, which combines different numerical formats for computation. SEADA is a new methodology designed to optimize these mixed-precision DNNs specifically for multi-precision spatial architectures, which are hardware setups that can process multiple precision levels simultaneously.
✦ Why It Matters
Engineers can leverage SEADA to enhance DNN performance on diverse hardware setups, improving efficiency and speed.
Key Takeaways
- Utilize SEADA's mapping tool for optimal DNN workload distribution.
- Apply bit-level entropy for efficient precision assignment in layers.
- Leverage the analytical cost model to evaluate architecture performance.
- Explore design-space options for multi-precision architectures effectively.
How It Works
SEADA employs a configurable analytical cost model that evaluates the performance of multi-precision spatial architectures. It uses a fast mapping tool to identify near-optimal configurations for DNN workloads, while its per-layer precision selection methodology leverages bit-level entropy to determine the most efficient numerical precision for each layer.
This combination allows for a tailored approach to optimizing DNNs, balancing accuracy and resource constraints.