Add to ORKGA number of recent researches focus on designing accelerators for popular deep learning algorithms. Most of these algorithms heavily involve matrix multiplication. As a result, building a neural processing unit (NPU) beside the CPU to accelerate matrix multiplication is a popular approach. The NPU helps reduce the work done by the CPU, and often operates in parallel with the CPU, so in general, introducing the NPU gains performance. Furthermore, the NPU itself can be accelerated due to the fact that the majority operation in the NPU is multiply-add. As a result, in this project, we propose two methods to accelerate the NPU: (1) Replace the digital multiply-add unit in the NPU with time-domain analog and digital mixed-signal multiply-add uni...
130 pagesOver the past decade, machine learning (ML) with deep neural networks (DNNs) has become ext...
Many error resilient applications can be approximated using multi-layer perceptrons (MLPs) with insi...
Machine learning has gained success in many application domains including medical data analysis, fin...
A number of recent researches focus on designing accelerators for popular deep learning algorithms. ...
Deep Neural Networks (DNNs) have become a promising solution to inject AI in our daily lives from se...
As AI applications become more prevalent and powerful, the performance of deep learning neural netwo...
The proliferation of embedded Neural Processing Units (NPUs) is enabling the adoption of Tiny Machin...
Efficient implementation of deep neural networks (DNNs) on CPU-based systems is critical owing to th...
The use of neural networks, machine learning, or artificial intelligence, in its broadest and most c...
Deep neural networks have proven to be particularly effective in visual and audio recognition tasks....
We introduce Neuro.ZERO-a co-processor architecture consisting of a main microcontroller (MCU) that ...
In recent years, the need for the efficient deployment of Neural Networks (NN) on edge devices has b...
Deep neural networks (DNN) are achieving state-of-the-art performance in many artificial intelligenc...
Deep learning algorithms have seen success in a wide variety of applications, such as machine transl...
Deep learning is a rising topic at the edge of technology, with applications in many areas of our li...
130 pagesOver the past decade, machine learning (ML) with deep neural networks (DNNs) has become ext...
Many error resilient applications can be approximated using multi-layer perceptrons (MLPs) with insi...
Machine learning has gained success in many application domains including medical data analysis, fin...
A number of recent researches focus on designing accelerators for popular deep learning algorithms. ...
Deep Neural Networks (DNNs) have become a promising solution to inject AI in our daily lives from se...
As AI applications become more prevalent and powerful, the performance of deep learning neural netwo...
The proliferation of embedded Neural Processing Units (NPUs) is enabling the adoption of Tiny Machin...
Efficient implementation of deep neural networks (DNNs) on CPU-based systems is critical owing to th...
The use of neural networks, machine learning, or artificial intelligence, in its broadest and most c...
Deep neural networks have proven to be particularly effective in visual and audio recognition tasks....
We introduce Neuro.ZERO-a co-processor architecture consisting of a main microcontroller (MCU) that ...
In recent years, the need for the efficient deployment of Neural Networks (NN) on edge devices has b...
Deep neural networks (DNN) are achieving state-of-the-art performance in many artificial intelligenc...
Deep learning algorithms have seen success in a wide variety of applications, such as machine transl...
Deep learning is a rising topic at the edge of technology, with applications in many areas of our li...
130 pagesOver the past decade, machine learning (ML) with deep neural networks (DNNs) has become ext...
Many error resilient applications can be approximated using multi-layer perceptrons (MLPs) with insi...
Machine learning has gained success in many application domains including medical data analysis, fin...