In search of templates

This paper explores, both wit This study reflects a recent shift towards the study of early stages of expert memory acquisition for chess positions. Over the course of fifteen sessions, two subjects who knew virtually nothing about the game of chess were trained to memorise positions. Increase in recall performance and chunk size was captured by power functions, confirming predictions made by the template theory (Gobet & Simon, 1996, 1998, 2000). The human data was compared to that of a computer simulation run on CHREST (Chunk Hierarchy and REtrieval STructures), an implementation of the template theory. The model accounts for the pattern of results in the human data, although it underestimates the size of the largest chunks and the rate of learning. Evidence for the presence of templates in human subjects was found

Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions

In the past decade, Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance in various Artificial Intelligence tasks. To accelerate the experimentation and development of CNNs, several software frameworks have been released, primarily targeting power-hungry CPUs and GPUs. In this context, reconfigurable hardware in the form of FPGAs constitutes a potential alternative platform that can be integrated in the existing deep learning ecosystem to provide a tunable balance between performance, power consumption and programmability. In this paper, a survey of the existing CNN-to-FPGA toolflows is presented, comprising a comparative study of their key characteristics which include the supported applications, architectural choices, design space exploration methods and achieved performance. Moreover, major challenges and objectives introduced by the latest trends in CNN algorithmic research are identified and presented. Finally, a uniform evaluation methodology is proposed, aiming at the comprehensive, complete and in-depth evaluation of CNN-to-FPGA toolflows.Comment: Accepted for publication at the ACM Computing Surveys (CSUR) journal, 201