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A neural processor, also known as a Neural Processing Unit (NPU), is a specialized microprocessor designed specifically to perform artificial neural network (ANN) computations. Artificial neural networks are computational models that mimic the structure and function of biological neural networks, which are the networks of neurons in the brain that process and transmit information. A neural processor typically consists of many processing cores, or processing elements (PEs), interconnected in a highly parallel architecture. Each processing element is designed to perform a simple mathematical operation, such as addition or multiplication, and can communicate with other processing elements in the network. Neural processors are optimized for deep learning applications, which involve training and running large-scale neural networks to recognize patterns in data. They can perform computations at high speeds and with high energy efficiency, making them ideal for applications such as image and speech recognition, natural language processing, and autonomous vehicles. The increasing use of machine learning and deep learning in the various industry verticals is expected to boost the neural processor market globally.
One major impact of the pandemic on neural processing has been a shift towards remote work and collaboration. Many researchers have had to adapt to working from home and collaborating virtually, which has led to changes in research and development. Remote collaboration tools have become more important than ever, and researchers have had to find new ways to share data, models, and code securely and efficiently. Another impact of the pandemic has been changed in funding and resource allocation. With the economic uncertainty caused by the pandemic, many countries and companies have had to re-evaluate their priorities and allocate resources accordingly. This factor has led to some shifts in research focus, emphasizing areas directly related to the pandemic, such as epidemiology and vaccine development and neural processor playing a critical role in the rapid manufacturing and distribution of vaccines.
Furthermore, the pandemic has highlighted the importance of neural processing in addressing real-world problems. With the emergence of COVID-19, researchers have used neural processing techniques to model the spread of the virus, analyze medical data, and develop treatments and vaccines. This has increased demand and investment in the neural processor market globally.
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Neural processors are becoming popular in various applications such as fraud detection, financial forecasting and image detection. Neural processors have been widely used in various applications, including fraud detection and image optimization. Neural processors analyze patterns in large datasets to identify suspicious transactions or behaviour. Neural networks, such as image finishing or super-resolution, are also used in image optimization. Auto-encoders are neural networks trained to reconstruct input data, and they can be used to denoise images by reconstructing the image with the noise removed. Autoencoders can also be used for image super-resolution by training the network to reconstruct a high-resolution image from a low-resolution input, driving market growth.
Intel Corporation (U.S.), IBM Corporation (U.S.), Google Inc. (U.S.), Qualcomm Inc. (U.S.), CEVA Inc. (U.S.), NVIDIA Corporation (U.S.), Teradeep Inc. (U.S.), BrainChip Holdings Ltd (Australia), Graphcore (UK), and Advanced Micro Devices (AMD) Inc. (U.S.)
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