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1. DOMINO: Domain-Aware Model Calibration in Medical Image Segmentation

   https://doi.org/10.1007/978-3-031-16443-9_44  

   Stolte, Skylar E.; Kyle Volle; Aprinda Indahlastari; Alejandro Albizu; Adam, Woods J.; Kevin Brink; Matthew Hale; Ruogu Fang (September 2022, Medical Image Computing and Computer Assisted Intervention – MICCAI 2022)

   Wang, L.; Dou, Q.; Fletcher, P.T.; Speidel, S.; Li, S. (Ed.)

   Model calibration measures the agreement between the predicted probability estimates and the true correctness likelihood. Proper model calibration is vital for high-risk applications. Unfortunately, modern deep neural networks are poorly calibrated, compromising trustworthiness and reliability. Medical image segmentation particularly suffers from this due to the natural uncertainty of tissue boundaries. This is exasperated by their loss functions, which favor overconfidence in the majority classes. We address these challenges with DOMINO, a domain-aware model calibration method that leverages the semantic confusability and hierarchical similarity between class labels. Our experiments demonstrate that our DOMINO-calibrated deep neural networks outperform non-calibrated models and state-of-the-art morphometric methods in head image segmentation. Our results show that our method can consistently achieve better calibration, higher accuracy, and faster inference times than these methods, especially on rarer classes. This performance is attributed to our domain-aware regularization to inform semantic model calibration. These findings show the importance of semantic ties between class labels in building confidence in deep learning models. The framework has the potential to improve the trustworthiness and reliability of generic medical image segmentation models. The code for this article is available at: https://github.com/lab-smile/DOMINO. 

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2. SCORCH: Neural Architecture Search and Hardware Accelerator Co-design with Reinforcement Learning

   https://doi.org/10.1109/ISQED60706.2024.10528756  

   Liu, Siqin; Karanth, Avinash (April 2024, IEEE)

   The ability to automatically generate a neural network architecture and the corresponding hardware implementation to optimize both accuracy and performance characteristics (latency, power) simultaneously for edge-based Artificial Intelligence (AI) applications is becoming prevalent. As both neural architecture search (NAS) and hardware implementation have ample design space, it is very challenging to integrate with resource-constrained edge computing hardware since the current co-search frameworks take several hundreds of GPU hours to converge. In this paper, we propose SCORCH, a novel neural architecture search and hardware accelerator co-design framework with reinforcement learning to maximize accuracy, and increase energy efficiency and throughput while converging faster. By predicting hyperparameters of neural networks together with hardware resources, we use a reinforcement-based multi-phased controller to explore neural architecture to achieve higher accuracy and hardware performance simultaneously by applying customized dataflows, voltage/frequency scaling, and tunable Network-on-Chip (NoC) hardware parameters. Our simulation results on the CIFAR-10/100 and ImageNet datasets show that SCORCH achieves identical neural network accuracy while achieving 2.6% higher accuracy, and 35.6%, 26.2%, and 65.8% reductions in latency, energy, and area compared with state-of-art co-search frameworks such as DANCE, NANDS, and NASAIC. 

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3. Tiny Eats: Eating Detection on a Microcontroller

   https://doi.org/10.1109/SenSysML50931.2020.00011  

   Nyamukuru, Maria T.; Odame, Kofi M. (April 2020, IEEE Workshop on Machine Learning on Edge in Sensor Systems (SenSys-ML))

   There is a growing interest in low power highly efficient wearable devices for automatic dietary monitoring (ADM) [1]. The success of deep neural networks in audio event classification problems makes them ideal for this task. Deep neural networks are, however, not only computationally intensive and energy inefficient but also require a large amount of memory. To address these challenges, we propose a shallow gated recurrent unit (GRU) architecture suitable for resource-constrained applications. This paper describes the implementation of the Tiny Eats GRU, a shallow GRU neural network, on a low power microcontroller, Arm Cortex M0+, to classify eating episodes. Tiny Eats GRU is a hybrid of the traditional GRU [2] and eGRU [3] which makes it small and fast enough to fit on the Arm Cortex M0+ with comparable accuracy to the traditional GRU. The Tiny Eats GRU utilizes only 4% of the Arm Cortex M0+ memory and identifies eating or non-eating episodes with 6 ms latency and accuracy of 95.15%. 

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4. Toward Efficient and Adaptive Design of Video Detection System with Deep Neural Networks

   https://doi.org/10.1145/3484946  

   Mao, Jiachen; Yang, Qing; Li, Ang; Nixon, Kent W.; Li, Hai; Chen, Yiran (May 2022, ACM Transactions on Embedded Computing Systems)

   In the past decade, Deep Neural Networks (DNNs), e.g., Convolutional Neural Networks, achieved human-level performance in vision tasks such as object classification and detection. However, DNNs are known to be computationally expensive and thus hard to be deployed in real-time and edge applications. Many previous works have focused on DNN model compression to obtain smaller parameter sizes and consequently, less computational cost. Such methods, however, often introduce noticeable accuracy degradation. In this work, we optimize a state-of-the-art DNN-based video detection framework—Deep Feature Flow (DFF) from the cloud end using three proposed ideas. First, we propose Asynchronous DFF (ADFF) to asynchronously execute the neural networks. Second, we propose a Video-based Dynamic Scheduling (VDS) method that decides the detection frequency based on the magnitude of movement between video frames. Last, we propose Spatial Sparsity Inference, which only performs the inference on part of the video frame and thus reduces the computation cost. According to our experimental results, ADFF can reduce the bottleneck latency from 89 to 19 ms. VDS increases the detection accuracy by 0.6% mAP without increasing computation cost. And SSI further saves 0.2 ms with a 0.6% mAP degradation of detection accuracy. 

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5. MobiEye: An Efficient Cloud-based Video Detection System for Real-time Mobile Applications

   https://doi.org/10.1145/3316781.3317865  

   Mao, Jiachen; Yang, Qing; Li, Ang; Li, Hai; Chen, Yiran (June 2019, Design Automation Conference)

   In recent years, machine learning research has largely shifted focus from the cloud to the edge. While the resulting algorithm- and hardware-level optimizations have enabled local execution for the majority of deep neural networks (DNNs) on edge devices, the sheer magnitude of DNNs associated with real-time video detection workloads has forced them to remain relegated to remote execution in the cloud. This problematic when combined with the strict latency requirements that are coupled with these workloads, and imposes a unique set of challenges not directly addressed in prior works. In this work, we design MobiEye, a cloud-based video detection system optimized for deployment in real-time mobile applications. MobiEye is able to achieve up to a 32% reduction in latency when compared to a conventional implementation of video detection system with only a marginal reduction in accuracy. 

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