Neural-Network-Aided Engineering: New Frontiers in Automation

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Deep neural networks (DNN) are receiving tremendous attention because of the recent convergence in the availability of computing power and data. DNN excel at machine learning (ML): the many layers in DNN can learn patterns at many scales. However, more layers also implies: more data is required for training; and, inference takes longer. Advances in computing, from mobile devices to warehouse-scale clusters, means DNN are no longer impractical. Furthermore, there's never been more data. The era of DNN has arrived. We previously developed OPAL, which automatically designs and optimizes DNN by employing deep learning to determine the relationship between: DNN structure and behavior, and DNN performance. OPAL has been demonstrated optimizing accuracy and GPGPU inference energy on benchmark datasets. We propose to significantly extend OPAL to automatically design (1) DNN for ML performance- and cost-constrained applications in the IoT, (2) trustworthy DNN that can tolerate input irregularity and hardware failure, and (3) embedded multiprocessor systems-on-chips (MPSoC) optimized under power, thermal, and reliability constraints. First, we will develop a general framework in OPAL for specifying and implementing new objective (or, cost) functions. Alternative cost functions are needed when designing (a) for real-time constraints, or (b) anything without a high-performance GPGPU: i.e., most IoT systems. We will investigate the effect of predicting multiple, related objectives; we will then optimize DNN for new targets, including accelerators, real-time, embedded, and configurable systems. Second, we will devise metrics for measuring the effect that: (a) missing, or uncertain, input data; and, (b) hardware failure during inference; have on DNN performance. Such concerns arise in real data and environments but are not captured by benchmark data sets. We will develop methodology for measuring the resulting changes in accuracy, compensating for such issues, and ultimately optimizing systems to tolerate them. Third, we will use OPAL to design MPSoC. Given a target platform, designers assign application tasks to resources (mapping), and subsequently order their execution (scheduling). Of particular importance today is design under thermal or system lifetime constraints; we hypothesize that OPAL will efficiently learn the relationship between spatial task mapping and the resulting thermal and lifetime effects, finding better solutions faster. The benefits of this research for HQP and Canada are clear. Participating HQP will gain valuable experience with DNN and supporting infrastructure, positioning them well for the growing ML/IOT domain. Montreal and QC ML efforts will also benefit: our work will improve their existing solutions, and simplify the introduction of additional products and services by reducing the resources required for ML and IoT development.