Publications by Author: Zahra Azad

M. Buch, Z. Azad, A. Joshi, and V. J. Reddi, “AI Tax in Mobile SoCs: End-to-end Performance Analysis of Machine Learning in Smartphones,” in 2021 IEEE International Symposium on Performance Analysis of Systems and Software, ISPASS '21, Virtual, Stony Brook, NY, March 28-30, 2021. IEEE VersionAbstract
Mobile software is becoming increasingly feature rich, commonly being accessorized with the powerful decision making capabilities of machine learning (ML). To keep up with the consequently higher power and performance demands, system and hardware architects add specialized hardware units onto their system-on-chips (SoCs) coupled with frameworks to delegate compute optimally. While these SoC innovations are rapidly improving ML model performance and power efficiency, auxiliary data processing and supporting infrastructure to enable ML model execution can substantially alter the performance profile of a system. This work posits the existence of an AI tax, the time spent on non-model execution tasks. We characterize the execution pipeline of open source ML benchmarks and Android applications in terms of AI tax and discuss where performance bottlenecks may unexpectedly arise.
Z. Azad, et al., “An end-to-end RISC-V solution for ML on the edge using in-pipeline support,” in Boston Area Architecture (BARC) Workshop, 2020. Publisher's VersionAbstract
Machine Learning (ML) is widely used today in many mobile applications. To preserve user privacy, there is a need to perform ML inference on the mobile devices. Given that ML inference is a computationally intensive task, the common technique used in mobile devices is offloading the task to a neural accelerator. However, the speed-up gained from offloading these tasks on the accelerators is limited by the overhead of frequent host-accelerator communication. In this paper, we propose a complete end-to-end solution that uses in-pipeline machine learning processing unit for accelerating ML workloads. First we introduce the software infrastructure we developed to support compilation and execution of machine learning models used in TensorFlow Lite framework. Then we discuss the microarchitecture we plan to implement for supporting the execution of our vectorized machine learning kernels.
M. S. Louis, et al., “Towards Deep Learning using TensorFlow Lite on RISC-V,” Third Workshop on Computer Architecture Research with RISC-V (CARRV). 2019.Abstract

Deep neural networks have been extensively adopted for a myriad of applications due to their ability to learn patterns from large amounts of data. The desire to preserve user privacy and reduce user-perceived latency has created the need to perform deep neural network inference tasks on low-power consumer edge devices. Since such tasks often tend to be computationally intensive, offloading this compute from mobile/embedded CPU to a purposedesigned "Neural Processing Engines" is a commonly adopted solution for accelerating deep learning computations. While these accelerators offer significant speed-ups for key machine learning kernels, overheads resulting from frequent host-accelerator communication often diminish the net application-level benefit of this heterogeneous system. Our solution for accelerating such workloads involves developing ISA extensions customized for machine learning kernels and designing a custom in-pipeline execution unit for these specialized instructions. We base our ISA extensions on RISC-V: an open ISA specification that lends itself to such specializations. In this paper, we present the software infrastructure for optimizing neural network execution on RISC-V with ISA extensions. Our ISA extensions are derived from the RISC-V Vector ISA proposal, and we develop optimized implementations of the critical kernels such as convolution and matrix multiplication using these instructions. These optimized functions are subsequently added to the TensorFlow Lite source code and cross-compiled for RISC-V. We find that only a small set of instruction extensions achieves coverage over a wide variety of deep neural networks designed for vision and speech-related tasks. On average, our software implementation using the extended instructions set reduces the executed instruction count by 8X in comparison to baseline implementation. In parallel, we are also working on the hardware design of the inpipeline machine learning accelerator. We plan to open-source our software modifications to TF Lite, as well as the micro-architecture design in due course.