Add to ORKGRecently, accelerators for extremely quantized deep neural network (DNN) inference with operand widths as low as 1-bit have gained popularity due to their ability to largely cut down energy cost per inference. In this paper, a flexible SoC with mixed-precision support is presented. Contrary to the current trend of fixed-datapath accelerators, this architecture makes use of a flexible datapath based on a Transport-Triggered Architecture (TTA). The architecture is fully programmable using C. The accelerator has a peak energy efficiency of 35/67/405 fJ/op (binary, ternary, and 8-bit precision) and a throughput of 614/307/77 GOPS, which is unprecedented for a programmable architecture
none4siIEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS PROCEEDINGS 0271-4302 E079855Binary Neur...
Quantization, effective Neural Network architecture, and efficient accelerator hardware are three im...
Deep neural networks (DNN) are achieving state-of-the-art performance in many artificial intelligenc...
Recently, accelerators for extremely quantized deep neural network (DNN) inference with operand widt...
Accelerators designed for deep neural network (DNN) inference with extremely low operand widths, dow...
This electronic version was submitted by the student author. The certified thesis is available in th...
The deployment of Quantized Neural Networks (QNN) on advanced microcontrollers requires optimized so...
Recently, there has been a push to perform deep learning (DL) computations on the edge rather than t...
With the surging popularity of edge computing, the need to efficiently perform neural network infere...
Real-time inference of deep convolutional neural networks (CNNs) on embedded systems and SoCs would ...
Real-time inference of deep convolutional neural networks (CNNs) on embedded systems and SoCs would ...
The current trend for deep learning has come with an enormous computational need for billions of Mul...
Abstract Model quantization is a widely used technique to compress and accelerate deep neural netwo...
Deep neural networks have achieved impressive results in computer vision and machine learning. Unfor...
The current trend for deep learning has come with an enormous computational need for billions of Mul...
none4siIEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS PROCEEDINGS 0271-4302 E079855Binary Neur...
Quantization, effective Neural Network architecture, and efficient accelerator hardware are three im...
Deep neural networks (DNN) are achieving state-of-the-art performance in many artificial intelligenc...
Recently, accelerators for extremely quantized deep neural network (DNN) inference with operand widt...
Accelerators designed for deep neural network (DNN) inference with extremely low operand widths, dow...
This electronic version was submitted by the student author. The certified thesis is available in th...
The deployment of Quantized Neural Networks (QNN) on advanced microcontrollers requires optimized so...
Recently, there has been a push to perform deep learning (DL) computations on the edge rather than t...
With the surging popularity of edge computing, the need to efficiently perform neural network infere...
Real-time inference of deep convolutional neural networks (CNNs) on embedded systems and SoCs would ...
Real-time inference of deep convolutional neural networks (CNNs) on embedded systems and SoCs would ...
The current trend for deep learning has come with an enormous computational need for billions of Mul...
Abstract Model quantization is a widely used technique to compress and accelerate deep neural netwo...
Deep neural networks have achieved impressive results in computer vision and machine learning. Unfor...
The current trend for deep learning has come with an enormous computational need for billions of Mul...
none4siIEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS PROCEEDINGS 0271-4302 E079855Binary Neur...
Quantization, effective Neural Network architecture, and efficient accelerator hardware are three im...
Deep neural networks (DNN) are achieving state-of-the-art performance in many artificial intelligenc...