The full abstract for this thesis is available in the body of the thesis, and will be available when the embargo expires.
Also available via: University of British Columbia
The full abstract for this thesis is available in the body of the thesis, and will be available when the embargo expires.
Also available via: University of British Columbia
The full abstract for this thesis is available in the body of the thesis, and will be available when the embargo expires.
Also available via: University of British Columbia
The full abstract for this thesis is available in the body of the thesis, and will be available when the embargo expires.
Iraj Moghaddasi, Byeong-Gyu Nam
In recent years, deep neural networks (DNNs) have addressed new applications with intelligent autonomy, often achieving higher accuracy than human experts. This capability comes at the expense of the ever-increasing complexity of emerging DNNs, causing enormous challenges while d…
Stefan Scholze, Johannes Partzsch, Sebastian Höppner, Florian Kelber, Andreas Dixius, Marco Stolba, et al.
In deep learning, efficiency gets more and more important to compensate for the ongoing growth in model sizes and applications. Neuromorphic hardware has long been advocated as an upcoming alternative to deep networks, taking inspiration from the brain for achieving unprecedented…
Leandro Fiorin, Marco Ronzani, Cristina Silvano
Mixed-precision computation has been introduced in deep neural networks (DNNs) as an effective approach to reduce latency, energy consumption, and memory footprint. However, efficiently mapping mixed-precision networks onto multi-precision spatial architectures poses several chal…