Search Results for author:
Found 6 papers, 0 papers with code
From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks
To address these challenges, researchers have developed various model compression techniques such as model quantization and model pruning.
OPQ: Compressing Deep Neural Networks with One-shot Pruning-Quantization
Numerous network compression methods such as pruning and quantization are proposed to reduce the model size significantly, of which the key is to find suitable compression allocation (e. g., pruning sparsity and quantization codebook) of each layer.
Delving into Channels: Exploring Hyperparameter Space of Channel Bit Widths with Linear Complexity
We formulate the quantization of deep neural networks as a rate-distortion optimization problem, and present an ultra-fast algorithm to search the bit allocation of channels.
PSRR-MaxpoolNMS: Pyramid Shifted MaxpoolNMS with Relationship Recovery
Unlike convolutions which are inherently parallel, the de-facto standard for NMS, namely GreedyNMS, cannot be easily parallelized and thus could be the performance bottleneck in convolutional object detection pipelines.
Towards Effective 2-bit Quantization: Pareto-optimal Bit Allocation for Deep CNNs Compression
In this paper, we address an important problem of how to optimize the bit allocation of weights and activations for deep CNNs compression.
Dataflow-based Joint Quantization of Weights and Activations for Deep Neural Networks
This paper addresses a challenging problem - how to reduce energy consumption without incurring performance drop when deploying deep neural networks (DNNs) at the inference stage.
