Mixed-precision neural networks (MPNNs) that enable the use of just enough data width for
a deep learning task promise significant advantages of both inference accuracy and …

The world has seen the great success of deep neural networks (DNNs) in a massive number
of artificial intelligence (AI) applications. However, develo** high-quality AI services to …

By quantizing weights with different precision for different parts of a network, mixed-precision
quantization promises to reduce the hardware cost and improve the speed of deep neural …

DSP blocks are undoubtedly efficient solutions for implementing multiply-accumulate (MAC)
operations on FPGA. Since DSP resources are scarce in FPGA, the advanced solution is to …

The lower power object detection challenge (LPODC) at the IEEE/ACM Design Automation
Conference is a premier contest in low-power object detection and algorithm (software) …

This paper introduces SDA, the first effort to adapt the expensive stable diffusion (SD) model
for edge FPGA deployment. First, we apply quantization-aware training to quantize its …

As the complexity and computational demands of deep learning models rise, the need for
effective optimization methods for neural network designs becomes paramount. This work …

Modern transformer-based deep neural networks present unique technical challenges for
effective acceleration in real-world applications. Apart from the vast amount of linear …

Many studies have demonstrated that 4-bit precision quantization can maintain accuracy
levels comparable to those of floating-point deep neural networks (DNNs). Thus, it has …

Mixed-precision neural network (MPNN) that utilizes just enough data width for the neural
network processing is an effective approach to meet the stringent resources constraints …