Accelerated training for cnn distributed deep learning through automatic resource-aware layer placement
The Convolutional Neural Network (CNN) model, often used for image classification,
requires significant training time to obtain high accuracy. To this end, distributed training is …
requires significant training time to obtain high accuracy. To this end, distributed training is …
Optimization Ways in Neural Network Compression
R Zhou, P Quan - Procedia Computer Science, 2023 - Elsevier
Deep learning is a powerful tool that uses simple representations to express complex ideas
and allows computers to mine hidden information and value from experience. It has …
and allows computers to mine hidden information and value from experience. It has …
Study of RRAM-Based Binarized Neural Networks Inference Accelerators Using an RRAM Physics-Based Compact Model
In-memory computing hardware accelerators for binarized neural networks based on
resistive RAM (RRAM) memory technologies represent a promising solution for enabling the …
resistive RAM (RRAM) memory technologies represent a promising solution for enabling the …
Quantization Method Integrated with Progressive Quantization and Distillation Learning
H Huang, B Pan, L Wang, C Jiang - Procedia Computer Science, 2023 - Elsevier
This paper proposed a quantization method based on the integration of progressive
quantization and distillation learning, aiming to address the shortcomings of traditional …
quantization and distillation learning, aiming to address the shortcomings of traditional …
Hardware-Algorithm Co-optimizations
B Moons, D Bankman, M Verhelst, B Moons… - … and Circuits for Always …, 2019 - Springer
As discussed in Chap. 1, neural network-based applications are still too costly for them to be
embedded on mobile and always-on devices. This chapter discusses hardware aware …
embedded on mobile and always-on devices. This chapter discusses hardware aware …