A Design Flow Framework for Fully-Connected Neural Networks Rapid Prototyping
| Autor: | Kostas Siozios, Konstantina Koliogeorgi, Nikolaos Zompakis, Georgios Zervakis, Dimitrios Anagnostos |
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| Rok vydání: | 2019 |
| Předmět: |
010302 applied physics
Rapid prototyping Artificial neural network Computer science 020208 electrical & electronic engineering Design flow Activation function 02 engineering and technology 01 natural sciences Computer architecture 0103 physical sciences VHDL 0202 electrical engineering electronic engineering information engineering Latency (engineering) Field-programmable gate array computer Resource utilization computer.programming_language |
| Zdroj: | COINS |
| Popis: | The current work deploys a framework for rapid prototyping of Fully-Connected Neural Networks (FCNs). The scope is to provide an automatic design flow that generates a template-based VHDL code considering the accuracy, the resource utilization and the design complexity. More precisely, the deployed tool incorporates hardware optimizations in the implementation of the multiplications, the activation function and the definition of the fixed-point types providing user-defined configurations thought a GUI. The FCNs of two applications (Alexnet and Lenet) were implemented to evaluate our approach. The results seem promising and prove the design flexibility of our framework generating optimized code that exceeds the 10K lines for each hardware instance within a few hours, while preserving low levels of latency that does not exceed 400 cycles for our applications. |
| Databáze: | OpenAIRE |
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