An Empirical Study of Low Precision Quantization for TinyML

no code implementations • 10 Mar 2022 • Shaojie Zhuo, Hongyu Chen, Ramchalam Kinattinkara Ramakrishnan, Tommy Chen, Chen Feng, Yicheng Lin, Parker Zhang, Liang Shen

In this study, we focus on post-training quantization (PTQ) algorithms that quantize a model to low-bit (less than 8-bit) precision with only a small set of calibration data and benchmark them on different tinyML use cases.

BIG-bench Machine Learning Model Compression +1

Differentiable Mask for Pruning Convolutional and Recurrent Networks

no code implementations • 10 Sep 2019 • Ramchalam Kinattinkara Ramakrishnan, Eyyüb Sari, Vahid Partovi Nia

Pruning is one of the most effective model reduction techniques.

Model Compression Multi-Task Learning +1

Deep Demosaicing for Edge Implementation

no code implementations • 26 Mar 2019 • Ramchalam Kinattinkara Ramakrishnan, Shangling Jui, Vahid Patrovi Nia

We provide an exhaustive search of deep neural network architectures and obtain a pareto front of Color Peak Signal to Noise Ratio (CPSNR) as the performance criterion versus the number of parameters as the model complexity that beats the state-of-the-art.

Demosaicking Neural Architecture Search