Optimization Techniques for Powerful yet Tiny Machine Learning Models
Can machine learning models be both powerful and tiny? Join us in this episode of TinyML Talks, where we uncover groundbreaking techniques for making machine learning more efficient through high-level synthesis. We sit down with Russell Clayne, Technical Director at Siemens EDA, who guides us through the intricate process of pruning convolutional and deep neural networks. Discover how post-training quantization and quantization-aware training can trim down models without sacrificing performance, making them perfect for custom hardware accelerators like FPGAs and ASICs.
From there, we dive…
The skinny
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