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Due to high accuracy, inherent redundancy, and embarrassingly parallel nature, the neural networks are fast becoming mainstream machine learning algorithms. However, these advantages come at the cost of high memory and processing requirements (that can be met by either GPUs, FPGAs or ASICs). For embedded systems, the requirements are particularly challenging because of stiff power and timing budgets...
It is well known that ASICs have orders of magnitude higher power efficiency than general propose processors. However, due to the high engineering and manufacturing cost only handful of companies can afford to design ASICs. To reduce this cost numerous high-level synthesis tools have emerged since last 2-3 decades. In spite of these tools, ASIC design is still considered expensive because they fail...
Today, machine learning based on neural networks has become mainstream, in many application domains. A small subset of machine learning algorithms, called Convolutional Neural Networks (CNN), are considered as state-ofthe- art for many applications (e.g. video/audio classification). The main challenge in implementing the CNNs, in embedded systems, is their large computation, memory, and bandwidth...
In the era of platforms hosting multiple applications with arbitrary reconfiguration requirements, static configuration architectures are neither optimal nor desirable. The static reconfiguration architectures either incur excessive overheads or cannot support advanced features (like time-sharing and runtime parallelism). As a solution to this problem, we present a polymorphic configuration architecture...
Today, Coarse Grained Reconfigurable Architectures (CGRAs) host multiple applications. Novel CGRAs allow each application to exploit runtime parallelism and time sharing. Although these features enhance the power and silicon efficiency, they significantly increase the configuration memory overheads (up to 50% area of the overall platform). As a solution to this problem researchers have employed statistical...
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