Beginner musicians often struggle to identify specific errors in their performances, such as playing incorrect notes or rhythms. There are two limitations in existing tools for music error detection: (1) Existing approaches rely on automatic alignment; therefore, they are prone to errors caused by small deviations between alignment targets; (2) There is insufficient data to train music error detection models, resulting in over-reliance on heuristics. To address (1), we propose a novel transformer model, Polytune, that takes audio inputs and outputs annotated music scores. This model can be trained end-to-end to implicitly align and compare performance audio with music scores through latent space representations. To address (2), we present a novel data generation technique capable of creating large-scale synthetic music error datasets. Our approach achieves a 64.1% average Error Detection F1 score, improving upon prior work by 40 percentage points across 14 instruments. Additionally, our model can handle multiple instruments compared with existing transcription methods repurposed for music error detection.

This paper investigates how to efficiently deploy vision transformers on edge devices for small workloads. Recent methods reduce the latency of transformer neural networks by removing or merging tokens with small accuracy degradation. However these methods are not designed with edge device deployment in mind: they do not leverage information about the latency-workload trends to improve efficiency. We address this shortcoming in our work. First we identify factors that affect ViT latency-workload relationships. Second we determine token pruning schedule by leveraging non-linear latency-workload relationships. Third we demonstrate a training-free token pruning method utilizing this schedule. We show other methods may increase latency by 2-30% while we reduce latency by 9-26%. For similar latency (within 5.2% or 7ms) across devices we achieve 78.6%-84.5% ImageNet1K classification accuracy while the state-of-the-art Token Merging achieves 45.8%-85.4%.

We propose Vision Token Turing Machines (ViTTM), an efficient, low-latency, memory-augmented Vision Transformer (ViT). Our approach builds on Neural Turing Machines (NTM) and Token Turing Machines (TTM), which were applied to NLP and sequential visual understanding tasks. ViTTMs are designed for non-sequential computer vision tasks such as image classification and segmentation. Our model creates two sets of tokens: process tokens and memory tokens; process tokens pass through encoder blocks and read-write from memory tokens at each encoder block in the network, allowing them to store and retrieve information from memory. By ensuring that there are fewer process tokens, we are able to reduce the inference time of the network while maintaining its accuracy. On ImageNet-1K, the state-of-the-art ViT-B has median latency of 529.5 ms and 81.0% accuracy, while our ViTTM-B is 56% faster (234.1 ms), with 2.4× fewer FLOPs, with an accuracy of 82.9%. On ADE20K semantic segmentation, ViT-B achieves 45.65 mIoU at 13.8 frames per second (FPS) whereas our ViTTM-B model achieves 45.17 mIoU with 26.8 FPS (+94%).

Computer vision often uses highly accurate Convolutional Neural Networks (CNNs), but these deep learning models are associated with ever-increasing energy and computation requirements. Producing more energy-efficient CNNs often requires model training which can be cost-prohibitive. We propose a novel, automated method to make a pretrained CNN more energy-efficient without re-training. Given a pretrained CNN, we insert a threshold layer that filters activations from the preceding layers to identify regions of the image that are irrelevant, i.e. can be ignored by the following layers while maintaining accuracy. Our modified focused convolution operation saves inference latency (by up to 25%) and energy costs (by up to 22%) on various popular pretrained CNNs, with little to no loss in accuracy.

Privacy concerns arise from malicious attacks on Deep Neural Network (DNN) applications during sensitive data inference on edge devices. Membership Inference Attack (MIA) is developed by adversaries to determine whether sensitive data is used to train the DNN applications. Prior work uses Trusted Execution Environments (TEEs) to hide DNN model inference from adversaries on edge devices. Unfortunately, existing methods have two major problems. First, due to the restricted memory of TEEs, prior work cannot secure large-size DNNs from gradient-based MIAs. Second, prior work is ineffective on output-based MIAs. To mitigate the problems, we present a depth-wise layer partitioning method to run large sensitive layers inside TEEs. We further propose a model quantization strategy to improve the defense capability of DNNs against output-based MIAs and accelerate the computation. We also automate the process of securing PyTorch-based DNN models inside TEEs. Experiments on Raspberry Pi 3B+ show that our method can reduce the accuracy of gradient-based MIAs on AlexNet, VGG-16, and ResNet-20 evaluated on the CIFAR-100 dataset by 28.8%, 11%, and 35.3%. The accuracy of output-based MIAs on the three models is also reduced by 18.5%, 13.4%, and 29.6%, respectively.

Software engineers develop, fine-tune, and deploy deep learning (DL) models using a variety of development frameworks and runtime environments. DL model converters move models between frameworks and to runtime environments. Conversion errors compromise model quality and disrupt deployment. However, the failure characteristics of DL model converters are unknown, adding risk when using DL interoperability technologies. This paper analyzes failures in DL model converters. We survey software engineers about DL interoperability tools, use cases, and pain points (N=92). Then, we characterize failures in model converters associated with the main interoperability tool, ONNX (N=200 issues in PyTorch and TensorFlow). Finally, we formulate and test two hypotheses about structural causes for the failures we studied. We find that the node conversion stage of a model converter accounts for ∼75% of the defects and 33% of reported failure are related to semantically incorrect models. The cause of semantically incorrect models is elusive, but models with behaviour inconsistencies share operator sequences. Our results motivate future research on making DL interoperability software simpler to maintain, extend, and validate. Research into behavioural tolerances and architectural coverage metrics would be fruitful.

Due to the cost of developing and training deep learning models from scratch, machine learning engineers have begun to reuse pre-trained models (PTMs) and fine-tune them for downstream tasks. PTM registries known as “model hubs” support engineers in distributing and reusing deep learning models. PTM packages include pre-trained weights, documentation, model architectures, datasets, and metadata. Mining the information in PTM packages will enable the discovery of engineering phenomena and tools to support software engineers. However, accessing this information is difficult — there are many PTM registries, and both the registries and the individual packages may have rate limiting for accessing the data.We present an open-source dataset, PTMTorrent, to facilitate the evaluation and understanding of PTM packages. This paper describes the creation, structure, usage, and limitations of the dataset. The dataset includes a snapshot of 5 model hubs and a total of 15,913 PTM packages. These packages are represented in a uniform data schema for cross-hub mining. We describe prior uses of this data and suggest research opportunities for mining using our dataset.The PTMTorrent dataset (v1) is available at: https://app.globus.org/file-manager?origin_id=55e17a6e-9d8f-11ed-a2a2-8383522b48d9&origin_path=%2F%7E%2F.Our dataset generation tools are available on GitHub: https://doi.org/10.5281/zenodo.7570357