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Neural-Network Hardware Drives the Latest Machine-Learning Craze

#artificialintelligence

Artificial-intelligence (AI) research covers a number of topics, including machine learning (ML). ML covers a lot of ground as well, from rule-based expert systems to the latest hot trend--neural networks. Neural networks are changing how developers solve problems, whether it be self-driving cars or the industrial Internet of Things (IIoT). Neural networks come in many forms, but deep neural networks (DNNs) are the most important at this point. A DNN consists of multiple layers, including input and output layers plus multiple hidden layers (Figure 1).


Machine Learning Using Hardware and Software

#artificialintelligence

For developers, advances in hardware and software for machine learning (ML) promise to bring these sophisticated methods to Internet of Things (IoT) edge devices. As this field of research evolves, however, developers can easily find themselves immersed in the deep theory behind these techniques instead of focusing on currently available solutions to help them get an ML-based design to market. To help designers get moving more quickly, this article briefly reviews the objectives and capabilities of ML, the ML development cycle, and the architecture of a basic fully connected neural network and a convolutional neural network (CNN). It then discusses the frameworks, libraries, and drivers that are enabling mainstream ML applications. It concludes by showing how general purpose processors and FPGAs can serve as the hardware platform for implementing machine learning algorithms.


TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems

arXiv.org Artificial Intelligence

Deep learning inference on embedded devices is a burgeoning field with myriad applications because tiny embedded devices are omnipresent. But we must overcome major challenges before we can benefit from this opportunity. Embedded processors are severely resource constrained. Their nearest mobile counterparts exhibit at least a 100---1,000x difference in compute capability, memory availability, and power consumption. As a result, the machine-learning (ML) models and associated ML inference framework must not only execute efficiently but also operate in a few kilobytes of memory. Also, the embedded devices' ecosystem is heavily fragmented. To maximize efficiency, system vendors often omit many features that commonly appear in mainstream systems, including dynamic memory allocation and virtual memory, that allow for cross-platform interoperability. The hardware comes in many flavors (e.g., instruction-set architecture and FPU support, or lack thereof). We introduce TensorFlow Lite Micro (TF Micro), an open-source ML inference framework for running deep-learning models on embedded systems. TF Micro tackles the efficiency requirements imposed by embedded-system resource constraints and the fragmentation challenges that make cross-platform interoperability nearly impossible. The framework adopts a unique interpreter-based approach that provides flexibility while overcoming these challenges. This paper explains the design decisions behind TF Micro and describes its implementation details. Also, we present an evaluation to demonstrate its low resource requirement and minimal run-time performance overhead.


Artificial Neural Networks and Neural Networks Applications - XenonStack

#artificialintelligence

Artificial Neural Networks are the computational models inspired by the human brain. Many of the recent advancements have been made in the field of Artificial Intelligence, including Voice Recognition, Image Recognition, Robotics using Artificial Neural Networks. These biological methods of computing are considered to be the next major advancement in the Computing Industry. The term'Neural' is derived from the human (animal) nervous system's basic functional unit'neuron' or nerve cells which are present in the brain and other parts of the human (animal) body. It receives signals from other neurons. It sums all the incoming signals to generate input.


Artificial Neural Networks and Neural Networks Applications - XenonStack Blog

#artificialintelligence

Artificial Neural Networks are the computational models inspired by the human brain. Many of the recent advancements have been made in the field of Artificial Intelligence, including Voice Recognition, Image Recognition, Robotics using Artificial Neural Networks. These biological methods of computing are considered to be the next major advancement in the Computing Industry. The term'Neural' is derived from the human (animal) nervous system's basic functional unit'neuron' or nerve cells which are present in the brain and other parts of the human (animal) body. It receives signals from other neurons. It sums all the incoming signals to generate input.