arXiv subjects

Paul N. Whatmough

Publications and source records attributed to Paul N. Whatmough.

21 records · Page 2Linked to original sources

arXiv2019

SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers

The vast majority of processors in the world are actually microcontroller units (MCUs), which find widespread use performing simple control tasks in applications ranging from automobiles to medical devices and office equipment. The Internet of Things (IoT) promises to inject machine learning into many of these every-day objects via tiny, cheap MCUs. However, these resource-impoverished hardware platforms severely limit the complexity of machine learning models that can be deployed. For example, although convolutional neural networks (CNNs) achieve state-of-the-art results on many visual recognition tasks, CNN inference on MCUs is challenging due to severe finite memory limitations. To circumvent the memory challenge associated with CNNs, various alternatives have been proposed that do fit within the memory budget of an MCU, albeit at the cost of prediction accuracy. This paper challenges the idea that CNNs are not suitable for deployment on MCUs. We demonstrate that it is possible to automatically design CNNs which generalize well, while also being small enough to fit onto memory-limited MCUs. Our Sparse Architecture Search method combines neural architecture search with pruning in a single, unified approach, which learns superior models on four popular IoT datasets. The CNNs we find are more accurate and up to $4.35\times$ smaller than previous approaches, while meeting the strict MCU working memory constraint.

cs.LG↗

arXiv2019

FixyNN: Efficient Hardware for Mobile Computer Vision via Transfer Learning

The computational demands of computer vision tasks based on state-of-the-art Convolutional Neural Network (CNN) image classification far exceed the energy budgets of mobile devices. This paper proposes FixyNN, which consists of a fixed-weight feature extractor that generates ubiquitous CNN features, and a conventional programmable CNN accelerator which processes a dataset-specific CNN. Image classification models for FixyNN are trained end-to-end via transfer learning, with the common feature extractor representing the transfered part, and the programmable part being learnt on the target dataset. Experimental results demonstrate FixyNN hardware can achieve very high energy efficiencies up to 26.6 TOPS/W ($4.81 \times$ better than iso-area programmable accelerator). Over a suite of six datasets we trained models via transfer learning with an accuracy loss of $<1\%$ resulting in up to 11.2 TOPS/W - nearly $2 \times$ more efficient than a conventional programmable CNN accelerator of the same area.

cs.CV↗

arXiv2014

Precision-Energy-Throughput Scaling Of Generic Matrix Multiplication and Convolution Kernels Via Linear Projections

Generic matrix multiplication (GEMM) and one-dimensional convolution/cross-correlation (CONV) kernels often constitute the bulk of the compute- and memory-intensive processing within image/audio recognition and matching systems. We propose a novel method to scale the energy and processing throughput of GEMM and CONV kernels for such error-tolerant multimedia applications by adjusting the precision of computation. Our technique employs linear projections to the input matrix or signal data during the top-level GEMM and CONV blocking and reordering. The GEMM and CONV kernel processing then uses the projected inputs and the results are accumulated to form the final outputs. Throughput and energy scaling takes place by changing the number of projections computed by each kernel, which in turn produces approximate results, i.e. changes the precision of the performed computation. Results derived from a voltage- and frequency-scaled ARM Cortex A15 processor running face recognition and music matching algorithms demonstrate that the proposed approach allows for 280%~440% increase of processing throughput and 75%~80% decrease of energy consumption against optimized GEMM and CONV kernels without any impact in the obtained recognition or matching accuracy. Even higher gains can be obtained if one is willing to tolerate some reduction in the accuracy of the recognition and matching applications.

cs.MM↗