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cs.CV2023
CRISP: Hybrid Structured Sparsity for Class-aware Model Pruning
Shivam Aggarwal, Kuluhan Binici, Tulika Mitra
Machine learning pipelines for classification tasks often train a universal model to achieve accuracy across a broad range of classes. However, a typical user encounters only a lim…
cs.CV2023
Shedding the Bits: Pushing the Boundaries of Quantization with Minifloats on FPGAs
Shivam Aggarwal, Hans Jakob Damsgaard, Alessandro Pappalardo +4
Post-training quantization (PTQ) is a powerful technique for model compression, reducing the numerical precision in neural networks without additional training overhead. Recent wor…