Block Floating Point (BFP) can efficiently support quantization for Deep Neural Network
(DNN) training by providing a wide dynamic range via a shared exponent across a group of …

This chapter presents HNPU, which is an energy-efficient DNN training processor by
adopting algorithm–hardware co-design. The HNPU supports stochastic dynamic fixed-point …

Recently, on-device training has become crucial for the success of edge intelligence.
However, frequent data movement between computing units and memory during training …

Transformer-based models achieve tremendous success in many artificial intelligence (AI)
tasks, outperforming conventional convolution neural networks (CNNs) from natural …

Recently, the efficient training of deep neural networks (DNNs) on resource-constrained
platforms has attracted increasing attention for protecting user privacy. However, it is still a …

On-device learning allows AI models to adapt to user data, thereby enhancing service
quality on edge platforms. However, training AI on resource-limited devices poses significant …

Many edge/mobile devices are now able to utilize deep neural networks (DNNs) thanks to
the development of mobile DNN accelerators. Mobile DNN accelerators overcame the …

On-chip training (OCT) accelerators improve personalized recognition accuracy while
ensuring user privacy. However, previous OCT accelerators often required significant …

Training deep neural networks (DNNs) on edge devices has attracted increasing attention in
real-world applications for domain adaption and privacy protection. However, deploying …

Edge device deep neural network (DNN) training is practical to improve model adaptivity for
unfamiliar datasets while avoiding privacy disclosure and huge communication cost …