In this paper, we present a novel approximate computing scheme suitable for realizing the
energy-efficient multiply-accumulate (MAC) processing. In contrast to the prior works that …

This paper presents an approximate computing method of long short-term memory (LSTM)
operations for energy-efficient end-to-end speech recognition. We newly introduce the …

This paper presents a novel stacking and multi-level indexing scheme for convolutional
neural networks (CNNs) used in energy-limited edge-level systems. Basically, the proposed …

Deep convolutional neural networks (CNNs) have shown strong abilities in the application of
artificial intelligence. However, due to their extensive amount of computation, traditional …

Convolutional neural networks (CNNs) play a crucial role in many EdgeAI and TinyML
applications, but their implementation usually requires external memory, which degrades the …

This paper presents a new algorithm-hardware co-optimization approach that maximizes
memory bandwidth utilization even for the pruned deep neural network (DNN) models …

A 0.13 mJ/prediction with 68.6% accuracy single-chip wired-logic artificial intelligence (AI)
processor is developed in a 16nm field-programmable gate array (FPGA). Compared with …

Convolutional neural network (CNN) accelerators are being widely used for their efficiency,
but they require a large amount of memory, leading to the use of a slow and power …

The development of the UAV field is changing with each passing day, especially a lot of
research has been done on the road of autonomous and controllable. At present, the control …

A 0.13 mJ/prediction with 68.6% accuracy wired-logic deep neural network (DNN) processor
is developed in a single 16-nm field-programmable gate array (FPGA) chip. Compared with …