Parameter Encoding on FPGAs Boosts Neural Network Efficiency
The key to creating more efficient neural network models is rooted in trimming and refining the many parameters in deep learning models without losing accuracy. Much of this work is happening on the software side, but devices like FPGAs that can be tuned for trimmed parameters are offering promising early results for implementation.
A team from UC San Diego has created a reconfigurable clustering approach to deep neural networks that encodes the parameters the network according the accuracy requirements and limitations of the platform—which are often bound by memory access bandwidth. Encoding the trimmed parameters in an FPGA resulted in …
Parameter Encoding on FPGAs Boosts Neural Network Efficiency was written by Nicole Hemsoth at The Next Platform.



