The recently proposed Conformer model has become the de facto backbone model for
various downstream speech tasks based on its hybrid attention-convolution architecture that …

Recent advances in state-of-the-art DNN architecture design have been moving toward
Transformer models. These models achieve superior accuracy across a wide range of …

The energy efficiency of deep neural network (DNN) inference can be improved with custom
accelerators. DNN inference accelerators often employ specialized hardware techniques to …

Approximate computing (AxC) has been long accepted as a design alternative for efficient
system implementation at the cost of relaxed accuracy requirements. Despite the AxC …

Power efficiency for license plate recognition (LPR) under unconstrained scenarios is a
crucial factor in many edge-based real-world applications, eg, autonomous vehicles whose …

Transformers have shown remarkable performance in both natural language processing
(NLP) and computer vision (CV) tasks. However, their real-time inference speed and …

Many state-of-the-art deep learning models for computer vision tasks are based on the
transformer architecture. Such models can be computationally expensive and are typically …

Non-linear functions are prevalent in Transformers and their lightweight variants, incurring
substantial and frequently underestimated hardware costs. Previous state-of-the-art works …

Transformer-based DNNs have dominated several AI fields with remarkable performance.
However, the scaling up of Transformer models up to trillions of parameters and computation …

The approximation of non-linear operation can simplify the logic design and save the system
resources during the neural network inference on Field-Programmable Gate Array (FPGA) …