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Full-Text Articles in Computer Engineering
Hardware For Quantized Mixed-Precision Deep Neural Networks, Andres Rios
Hardware For Quantized Mixed-Precision Deep Neural Networks, Andres Rios
Open Access Theses & Dissertations
Recently, there has been a push to perform deep learning (DL) computations on the edge rather than the cloud due to latency, network connectivity, energy consumption, and privacy issues. However, state-of-the-art deep neural networks (DNNs) require vast amounts of computational power, data, and energyâ??resources that are limited on edge devices. This limitation has brought the need to design domain-specific architectures (DSAs) that implement DL-specific hardware optimizations. Traditionally DNNs have run on 32-bit floating-point numbers; however, a body of research has shown that DNNs are surprisingly robust and do not require all 32 bits. Instead, using quantization, networks can run on …
Dedicated Hardware For Machine/Deep Learning: Domain Specific Architectures, Angel Izael Solis
Dedicated Hardware For Machine/Deep Learning: Domain Specific Architectures, Angel Izael Solis
Open Access Theses & Dissertations
Artificial intelligence has come a very long way from being a mere spectacle on the silver screen in the 1920s [Hml18]. As artificial intelligence continues to evolve, and we begin to develop more sophisticated Artificial Neural Networks, the need for specialized and more efficient machines (less computational strain while maintaining the same performance results) becomes increasingly evident. Though these new techniques, such as Multilayer Perceptrons, Convolutional Neural Networks and Recurrent Neural Networks, may seem as if they are on the cutting edge of technology, many of these ideas are over 60 years old! However, many of these earlier models, at …