Orpheus: A New Deep Learning Framework for Easy Deployment and Evaluation of Edge Inference

Optimising deep learning inference across edge devices and optimisation targets such as inference time, memory footprint and power consumption is a key challenge due to the ubiquity of neural networks. Today, production deep learning frameworks provide useful abstractions to aid machine learning engineers and systems researchers. However, in exchange they can suffer from compatibility challenges (especially on constrained platforms), inaccessible code complexity, or design choices that otherwise limit research from a systems perspective. This paper presents Orpheus, a new deep learning framework for easy prototyping, deployment and evaluation of inference optimisations. Orpheus features a small codebase, minimal dependencies, and a simple process for integrating other third party systems. We present some preliminary evaluation results.Comment: To be published as a poster in 2020 IEEE International Symposium on Performance Analysis of Systems and Softwar

The resistance experiments: Morality, authority and obedience in Stanley Milgram's account

The paper seeks to re‐conceptualize Stanley Milgram's (in)famous experiments on willing obedience by drawing solely on Milgram's own contemporary account. It identifies a substantial incongruence between the findings Milgram presented (i.e., his description of the experiments) and the meaning he imputed to them (i.e., his interpretation of the exper iments). It argues that instead of operationalizing the concepts he claimed to operationalize – legitimate authority, embodied morality and willing obedience –, Milgram's description suggests that the operative forces in the experiments were an illegitimate authority and acts which in effect collude with that authority. As a result, the paper concludes that what the experimental findings represented was not so much obedience out of choice, but out of coercion. Thus, the paper seeks to redirect the conceptual‐moral focus of the findings from the participants who “shockingly” obeyed to those who managed to resist the coercive force of the total experimental situation

Compiler-centric across-stack deep learning acceleration

Optimizing the deployment of Deep Neural Networks (DNNs) is hard. Despite deep learning approaches increasingly providing state-of-the-art solutions to a variety of difficult problems, such as computer vision and natural language processing, DNNs can be prohibitively expensive, for example, in terms of inference time or memory usage. Effective exploration of the design space requires a holistic approach, including a range of topics from machine learning, systems, and hardware. The rapid proliferation of deep learning applications has raised demand for efficient exploration and acceleration of deep learning based solutions. However, managing the range of optimization techniques, as well as how they interact with each other across the stack is a non-trivial task. A family of emerging specialized compilers for deep learning, tensor compilers, appear to be a strong candidate to help manage the complexity of across-stack optimization choices, and enable new approaches. This thesis presents new techniques and explorations of the Deep Learning Acceleration Stack (DLAS), with the perspective that the tensor compiler will increasingly be the center of this stack. First, we motivate the challenges in exploring DLAS, by describing the experience of running a perturbation study varying parameters at every layer of the stack. The core of the study is implemented using a tensor compiler, which reduces the complexity of evaluating the wide range of variants, although still requires a significant engineering effort to realize. Next, we develop a new algorithm for grouped convolution, a model optimization technique for which existing solutions provided poor inference time scaling. We implement and optimize our algorithm using a tensor compiler, outperforming existing approaches by 5.1× on average (arithmetic mean). Finally, we propose a technique, transfer-tuning, to reduce the search time required for automatic tensor compiler code optimization, reducing the search time required by 6.5× on average. The techniques and contributions of this thesis across these interconnected domains demonstrate the exciting potential of tensor compilers to simplify and improve design space exploration for DNNs, and their deployment. The outcomes of this thesis enable new lines of research to enable machine learning developers to keep up with the rapidly evolving landscape of neural architectures and hardware

DeltaNN: Assessing the Impact of Computational Environment Parameters on the Performance of Image Recognition Models

Image recognition tasks typically use deep learning and require enormous processing power, thus relying on hardware accelerators like GPUs and TPUs for fast, timely processing. Failure in real-time image recognition tasks can occur due to sub-optimal mapping on hardware accelerators during model deployment, which may lead to timing uncertainty and erroneous behavior. Mapping on hardware accelerators is done using multiple software components like deep learning frameworks, compilers, and device libraries, that we refer to as the computational environment. Owing to the increased use of image recognition tasks in safety-critical applications like autonomous driving and medical imaging, it is imperative to assess their robustness to changes in the computational environment, as the impact of parameters like deep learning frameworks, compiler optimizations, and hardware devices on model performance and correctness is not yet well understood. In this paper we present a differential testing framework, DeltaNN, that allows us to assess the impact of different computational environment parameters on the performance of image recognition models during deployment, post training. DeltaNN generates different implementations of a given image recognition model for variations in environment parameters, namely, deep learning frameworks, compiler optimizations and hardware devices and analyzes differences in model performance as a result. Using DeltaNN, we conduct an empirical study of robustness analysis of three popular image recognition models using the ImageNet dataset. We report the impact in terms of misclassifications and inference time differences across different settings. In total, we observed up to 72% output label differences across deep learning frameworks, and up to 81% unexpected performance degradation in terms of inference time, when applying compiler optimizations.Comment: 11 pages, 10 figures, 2 table

Fault Localization for Buggy Deep Learning Framework Conversions in Image Recognition

When deploying Deep Neural Networks (DNNs), developers often convert models from one deep learning framework to another (e.g., TensorFlow to PyTorch). However, this process is error-prone and can impact target model accuracy. To identify the extent of such impact, we perform and briefly present a differential analysis against three DNNs widely used for image recognition (MobileNetV2, ResNet101, and InceptionV3) converted across four well-known deep learning frameworks (PyTorch, Keras, TensorFlow (TF), and TFLite), which revealed numerous model crashes and output label discrepancies of up to 72%. To mitigate such errors, we present a novel approach towards fault localization and repair of buggy deep learning framework conversions, focusing on pre-trained image recognition models. Our technique consists of four stages of analysis: 1) conversion tools, 2) model parameters, 3) model hyperparameters, and 4) graph representation. In addition, we propose various strategies towards fault repair of the faults detected. We implement our technique on top of the Apache TVM deep learning compiler, and we test it by conducting a preliminary fault localization analysis for the conversion of InceptionV3 from TF to TFLite. Our approach detected a fault in a common DNN converter tool, which introduced precision errors in weights, reducing model accuracy. After our fault localization, we repaired the issue, reducing our conversion error to zero.Comment: 5 pages, 3 figures, 1 tabl

Fault Localization for Buggy Deep Learning Framework Conversions in Image Recognition

When deploying Deep Neural Networks (DNNs), developers often convert models from one deep learning framework to another (e.g., TensorFlow to PyTorch). However, this process is error-prone and can impact target model accuracy. To identify the extent of such impact, we perform and briefly present a differential analysis against three DNNs widely used for image recognition (MobileNetV2, ResNet101, and InceptionV3) converted across four well-known deep learning frameworks (PyTorch, Keras, TensorFlow (TF), and TFLite), which revealed numerous model crashes and output label discrepancies of up to 72%. To mitigate such errors, we present a novel approach towards fault localization and repair of buggy deep learning framework conversions, focusing on pre-trained image recognition models. Our technique consists of four stages of analysis: 1) conversion tools, 2) model parameters, 3) model hyperparameters, and 4) graph representation. In addition, we propose various strategies towards fault repair of the faults detected. We implement our technique on top of the Apache TVM deep learning compiler, and we test it by conducting a preliminary fault localization analysis for the conversion of InceptionV3 from TF to TFLite. Our approach detected a fault in a common DNN converter tool, which introduced precision errors in weights, reducing model accuracy. After our fault localization, we repaired the issue, reducing our conversion error to zero

DeltaNN: Assessing the Impact of Computational Environment Parameters on the Performance of Image Recognition Models

Image recognition tasks typically use deep learning and require enormous processing power, thus relying on hardware accelerators like GPUs and TPUs for fast, timely processing. Failure in real-time image recognition tasks can occur due to sub-optimal mapping on hardware accelerators during model deployment, which may lead to timing uncertainty and erroneous behavior. Mapping on hardware accelerators is done using multiple software components like deep learning frameworks, compilers, and device libraries, that we refer to as the computational environment. Owing to the increased use of image recognition tasks in safety-critical applications like autonomous driving and medical imaging, it is imperative to assess their robustness to changes in the computational environment, as the impact of parameters like deep learning frameworks, compiler optimizations, and hardware devices on model performance and correctness is not yet well understood. In this paper we present a differential testing framework, DeltaNN, that allows us to assess the impact of different computational environment parameters on the performance of image recognition models during deployment, post training. DeltaNN generates different implementations of a given image recognition model for variations in environment parameters, namely, deep learning frameworks, compiler optimizations and hardware devices and analyzes differences in model performance as a result. Using DeltaNN, we conduct an empirical study of robustness analysis of three popular image recognition models using the ImageNet dataset. We report the impact in terms of misclassifications and inference time differences across different settings. In total, we observed up to 72% output label differences across deep learning frameworks, and up to 81% unexpected performance degradation in terms of inference time, when applying compiler optimizations

Exploring Effects of Computational Parameter Changes to Image Recognition Systems

Image recognition tasks typically use deep learning and require enormous processing power, thus relying on hardware accelerators like GPUs and FPGAs for fast, timely processing. Failure in real-time image recognition tasks can occur due to incorrect mapping on hardware accelerators, which may lead to timing uncertainty and incorrect behavior. Owing to the increased use of image recognition tasks in safety-critical applications like autonomous driving and medical imaging, it is imperative to assess their robustness to changes in the computational environment as parameters like deep learning frameworks, compiler optimizations for code generation, and hardware devices are not regulated with varying impact on model performance and correctness. In this paper we conduct robustness analysis of four popular image recognition models (MobileNetV2, ResNet101V2, DenseNet121 and InceptionV3) with the ImageNet dataset, assessing the impact of the following parameters in the model's computational environment: (1) deep learning frameworks; (2) compiler optimizations; and (3) hardware devices. We report sensitivity of model performance in terms of output label and inference time for changes in each of these environment parameters. We find that output label predictions for all four models are sensitive to choice of deep learning framework (by up to 57%) and insensitive to other parameters. On the other hand, model inference time was affected by all environment parameters with changes in hardware device having the most effect. The extent of effect was not uniform across models.Comment: 9 pages, 8 figures, 1 tabl

Orpheus: a New Deep Learning Framework for Easy Deployment and Evaluation of Edge Inference

Optimising deep learning inference across edge devices and optimisation targets such as inference time, memory footprint and power consumption is a key challenge due to the ubiquity of neural networks. Today, production deep learning frameworks provide useful abstractions to aid machine learning engineers and systems researchers. However, in exchange they can suffer from compatibility challenges (especially on constrained platforms), inaccessible code complexity, or design choices that otherwise limit research from a systems perspective. This paper presents Orpheus, a new deep learning framework for easy prototyping, deployment and evaluation of inference optimisations. Orpheus features a small codebase, minimal dependencies, and a simple process for integrating other third party systems. We present some preliminary evaluation results

Productive Reproducible Workflows for DNNs: A Case Study for Industrial Defect Detection

As Deep Neural Networks (DNNs) have become an increasingly ubiquitous workload, the range of libraries and tooling available to aid in their development and deployment has grown significantly. Scalable, production quality tools are freely available under permissive licenses, and are accessible enough to enable even small teams to be very productive. However within the research community, awareness and usage of said tools is not necessarily widespread, and researchers may be missing out on potential productivity gains from exploiting the latest tools and workflows. This paper presents a case study where we discuss our recent experience producing an end-to-end artificial intelligence application for industrial defect detection. We detail the high level deep learning libraries, containerized workflows, continuous integration/deployment pipelines, and open source code templates we leveraged to produce a competitive result, matching the performance of other ranked solutions to our three target datasets. We highlight the value that exploiting such systems can bring, even for research, and detail our solution and present our best results in terms of accuracy and inference time on a server class GPU, as well as inference times on a server class CPU, and a Raspberry Pi 4