Experimental Demonstration of the Fixed-Point Sparse Coding Performance

The Sparse Coding (SC) model has been proved to be among the best neural networks which are mainly used in unsupervised feature learning for many applications. Running a sparse coding algorithm is a time-consuming task due to its large scale and processing characteristics, which naturally leads to investigating FPGA acceleration. Fixed-point arithmetic can be used when implementing SC in FPGAs to reduce the execution time, but the implications for accuracy are not clear. Previous studies have focused only on accelerators using some fixed bitwidths on other neural networks models. Our work gives a comprehensive evaluation to demonstrate the bit-width effect on SCs, achieving the best performance and area efficiency. The method of data format conversion and the matrix blocking are the main factors considered according to the situation of hardware implementation. The simulation method of the simple truncation, the representation of the domain constraint and the matrix blocking with different parallelism were evaluated in this paper. The results have shown that the fixedpoint bit-width did have effect on the performance of SC. We must limit the representation domain of the data carefully and select an available bit-width according to the computation parallelism. The result has also shown that using a fixed-point arithmetic can guarantee the precision of the SC algorithm and get acceptable convergence speed.