Add to ORKGThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 89-91).Model quantization provides considerable latency and energy consumption reductions while preserving accuracy. However, the optimal bitwidth reduction varies on a layer by layer basis. This thesis suggests a novel neural network accelerator architecture that handles multiple bit precisions for both weights and activations. The architecture is based on a fused spatial and temporal...
This work presents a dynamically reconfigurable architecture for Neural Network (NN) accelerators im...
This work presents a dynamically reconfigurable architecture for Neural Network (NN) accelerators im...
Convolution Neural Network (CNN) is a special kind of neural network that is inspired by the behavio...
Quantization, effective Neural Network architecture, and efficient accelerator hardware are three im...
Recently, accelerators for extremely quantized deep neural network (DNN) inference with operand widt...
IEEEWe introduce an area/energy-efficient precisionscalable neural network accelerator architecture....
Recently, accelerators for extremely quantized deep neural network (DNN) inference with operand widt...
Abstract Model quantization is a widely used technique to compress and accelerate deep neural netwo...
Quantized neural networks are well known for reducing latency, power consumption, and model size wit...
Neural networks have contributed significantly in applications that had been difficult to implement ...
The current trend for deep learning has come with an enormous computational need for billions of Mul...
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 ...
With the surging popularity of edge computing, the need to efficiently perform neural network infere...
Recently, there has been a push to perform deep learning (DL) computations on the edge rather than t...
This work presents a dynamically reconfigurable architecture for Neural Network (NN) accelerators im...
This work presents a dynamically reconfigurable architecture for Neural Network (NN) accelerators im...
Convolution Neural Network (CNN) is a special kind of neural network that is inspired by the behavio...
Quantization, effective Neural Network architecture, and efficient accelerator hardware are three im...
Recently, accelerators for extremely quantized deep neural network (DNN) inference with operand widt...
IEEEWe introduce an area/energy-efficient precisionscalable neural network accelerator architecture....
Recently, accelerators for extremely quantized deep neural network (DNN) inference with operand widt...
Abstract Model quantization is a widely used technique to compress and accelerate deep neural netwo...
Quantized neural networks are well known for reducing latency, power consumption, and model size wit...
Neural networks have contributed significantly in applications that had been difficult to implement ...
The current trend for deep learning has come with an enormous computational need for billions of Mul...
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 ...
With the surging popularity of edge computing, the need to efficiently perform neural network infere...
Recently, there has been a push to perform deep learning (DL) computations on the edge rather than t...
This work presents a dynamically reconfigurable architecture for Neural Network (NN) accelerators im...
This work presents a dynamically reconfigurable architecture for Neural Network (NN) accelerators im...
Convolution Neural Network (CNN) is a special kind of neural network that is inspired by the behavio...