Resumen de Low-power architectures for automatic speech recognition
Automatic Speech Recognition (ASR) is one of the most important applications in the area of cognitive computing. Fast and accurate ASR is emerging as a key application for mobile and wearable devices. These devices, such as smartphones, have incorporated speech recognition as one of the main interfaces for user interaction. This trend towards voice-based user interfaces is likely to continue in the next years which is changing the way of human-machine interaction.
Effective speech recognition systems require real-time recognition, which is challenging for mobile devices due to the compute-intensive nature of the problem and the power constraints of such systems and involves a huge effort for CPU architectures to reach it. GPU architectures offer parallelization capabilities which can be exploited to increase the performance of speech recognition systems. However, efficiently utilizing the GPU resources for speech recognition is also challenging, as the software implementations exhibit irregular and unpredictable memory accesses and poor temporal locality.
The purpose of this thesis is to study the characteristics of ASR systems running on low-power mobile devices in order to propose different techniques to improve performance and energy consumption. We propose several software-level optimizations driven by the power/performance analysis. Unlike previous proposals that trade accuracy for performance by reducing the number of Gaussians evaluated, we maintain accuracy and improve performance by effectively using the underlying CPU microarchitecture.
We use a refactored implementation of the GMM evaluation code to ameliorate the impact of branches. Then, we exploit the vector unit available on most modern CPUs to boost GMM computation, introducing a novel memory layout for storing the means and variances of the Gaussians in order to maximize the effectiveness of vectorization. In addition, we compute the Gaussians for multiple frames in parallel, significantly reducing memory bandwidth usage. Our experimental results show that the proposed optimizations provide 2.68x speedup over the baseline Pocketsphinx decoder on a high-end Intel Skylake CPU, while achieving 61% energy savings. On a modern ARM Cortex-A57 mobile processor our techniques improve performance by 1.85x, while providing 59% energy savings without any loss in the accuracy of the ASR system.
Secondly, we propose a register renaming technique that exploits register reuse to reduce the pressure on the register file. Our technique leverages physical register sharing by introducing minor changes in the register map table and the issue queue. We evaluated our renaming technique on top of a modern out-of-order processor. The proposed scheme supports precise exceptions and we show that it results in 9.5% performance improvements for GMM evaluation. Our experimental results show that the proposed register renaming scheme provides 6% speedup on average for the SPEC2006 benchmarks. Alternatively, our renaming scheme achieves the same performance while reducing the number of physical registers by 10.5%.
Finally, we propose a hardware accelerator for GMM evaluation that reduces the energy consumption by three orders of magnitude compared to solutions based on CPUs and GPUs. The proposed accelerator implements a lazy evaluation scheme where Gaussians are computed on demand, avoiding 50% of the computations. Furthermore, it employs a novel clustering scheme to reduce the size of the GMM parameters, which results in 8x memory bandwidth savings with a negligible impact on accuracy. Finally, it includes a novel memoization scheme that avoids 74.88% of floating-point operations. The end design provides a 164x speedup and 3532x energy reduction when compared with a highly-tuned implementation running on a modern mobile CPU. Compared to a state-of-the-art mobile GPU, the GMM accelerator achieves 5.89x speedup over a highly optimized CUDA implementation, while reducing energy by 241x.
