[Eecs_bscs] Offered in Spring EE 4900/5900: Hardware for Deep Learning

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Instructors: Kyle Shiflett and Avinash Karanth
Credit Hours: 3
EE 4900/5900 is intended to introduce students to basic deep neural networks, and provide an indepth
study of computer architecture methods for efficient training and inference of deep neural
networks. The recent proliferation of artificial intelligence, in particular deep neural networks
(DNNs), has led to an increasing pressure on hardware systems that run these models. DNNs
have established some of the leading state-of-the-art models for tasks such as image classification
and speech recognition, some even achieving super-human accuracy. As the size and complexity
of DNN models continue to grow, as does the need for energy-efficient and fast execution of these
workloads. This course focuses on recent computer architecture trends and hardware-software
co-design techniques that facilitate efficient execution of DNNs.
Topics covered in this course include:
* Multilayer perceptrons
* Convolutional neural networks
* Affine and nonlinear integer quantization
* Operand dataflow and stationarity
* Hardware accelerators
* Compression with sparsity and pruning
* Memory organization
* Interconnects
* Training
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