AssemAI: Interpretable Image-Based Anomaly Detection for Manufacturing Pipelines

Anomaly detection in manufacturing pipelines remains a critical challenge, intensified by the complexity and variability of industrial environments. This paper introduces AssemAI, an interpretable image-based anomaly detection system tailored for smart manufacturing pipelines. Our primary contributions include the creation of a tailored image dataset and the development of a custom object detection model, YOLO-FF, designed explicitly for anomaly detection in manufacturing assembly environments. Utilizing the preprocessed image dataset derived from an industry-focused rocket assembly pipeline, we address the challenge of imbalanced image data and demonstrate the importance of image-based methods in anomaly detection. The proposed approach leverages domain knowledge in data preparation, model development and reasoning. We compare our method against several baselines, including simple CNN and custom Visual Transformer (ViT) models, showcasing the effectiveness of our custom data preparation and pretrained CNN integration. Additionally, we incorporate explainability techniques at both user and model levels, utilizing ontology for user-friendly explanations and SCORE-CAM for in-depth feature and model analysis. Finally, the model was also deployed in a real-time setting. Our results include ablation studies on the baselines, providing a comprehensive evaluation of the proposed system. This work highlights the broader impact of advanced image-based anomaly detection in enhancing the reliability and efficiency of smart manufacturing processes.

HiRISE: High-Resolution Image Scaling for Edge ML via In-Sensor Compression and Selective ROI

With the rise of tiny IoT devices powered by machine learning (ML), many researchers have directed their focus toward compressing models to fit on tiny edge devices. Recent works have achieved remarkable success in compressing ML models for object detection and image classification on microcontrollers with small memory, e.g., 512kB SRAM. However, there remain many challenges prohibiting the deployment of ML systems that require high-resolution images. Due to fundamental limits in memory capacity for tiny IoT devices, it may be physically impossible to store large images without external hardware. To this end, we propose a high-resolution image scaling system for edge ML, called HiRISE, which is equipped with selective region-of-interest (ROI) capability leveraging analog in-sensor image scaling. Our methodology not only significantly reduces the peak memory requirements, but also achieves up to 17.7x reduction in data transfer and energy consumption.

Flex-TPU: A Flexible TPU with Runtime Reconfigurable Dataflow Architecture

Tensor processing units (TPUs) are one of the most well-known machine learning (ML) accelerators utilized at large scale in data centers as well as in tiny ML applications. TPUs offer several improvements and advantages over conventional ML accelerators, like graphical processing units (GPUs), being designed specifically to perform the multiply-accumulate (MAC) operations required in the matrix-matrix and matrix-vector multiplies extensively present throughout the execution of deep neural networks (DNNs). Such improvements include maximizing data reuse and minimizing data transfer by leveraging the temporal dataflow paradigms provided by the systolic array architecture. While this design provides a significant performance benefit, the current implementations are restricted to a single dataflow consisting of either input, output, or weight stationary architectures. This can limit the achievable performance of DNN inference and reduce the utilization of compute units. Therefore, the work herein consists of developing a reconfigurable dataflow TPU, called the Flex-TPU, which can dynamically change the dataflow per layer during run-time. Our experiments thoroughly test the viability of the Flex-TPU comparing it to conventional TPU designs across multiple well-known ML workloads. The results show that our Flex-TPU design achieves a significant performance increase of up to 2.75x compared to conventional TPU, with only minor area and power overheads.

Towards Efficient Deployment of Hybrid SNNs on Neuromorphic and Edge AI Hardware

This paper explores the synergistic potential of neuromorphic and edge computing to create a versatile machine learning (ML) system tailored for processing data captured by dynamic vision sensors. We construct and train hybrid models, blending spiking neural networks (SNNs) and artificial neural networks (ANNs) using PyTorch and Lava frameworks. Our hybrid architecture integrates an SNN for temporal feature extraction and an ANN for classification. We delve into the challenges of deploying such hybrid structures on hardware. Specifically, we deploy individual components on Intel's Neuromorphic Processor Loihi (for SNN) and Jetson Nano (for ANN). We also propose an accumulator circuit to transfer data from the spiking to the non-spiking domain. Furthermore, we conduct comprehensive performance analyses of hybrid SNN-ANN models on a heterogeneous system of neuromorphic and edge AI hardware, evaluating accuracy, latency, power, and energy consumption. Our findings demonstrate that the hybrid spiking networks surpass the baseline ANN model across all metrics and outperform the baseline SNN model in accuracy and latency.

Lightator: An Optical Near-Sensor Accelerator with Compressive Acquisition Enabling Versatile Image Processing

This paper proposes a high-performance and energy-efficient optical near-sensor accelerator for vision applications, called Lightator. Harnessing the promising efficiency offered by photonic devices, Lightator features innovative compressive acquisition of input frames and fine-grained convolution operations for low-power and versatile image processing at the edge for the first time. This will substantially diminish the energy consumption and latency of conversion, transmission, and processing within the established cloud-centric architecture as well as recently designed edge accelerators. Our device-to-architecture simulation results show that with favorable accuracy, Lightator achieves 84.4 Kilo FPS/W and reduces power consumption by a factor of ~24x and 73x on average compared with existing photonic accelerators and GPU baseline.

A Novel Stochastic LSTM Model Inspired by Quantum Machine Learning

Works in quantum machine learning (QML) over the past few years indicate that QML algorithms can function just as well as their classical counterparts, and even outperform them in some cases. Among the corpus of recent work, many current QML models take advantage of variational quantum algorithm (VQA) circuits, given that their scale is typically small enough to be compatible with NISQ devices and the method of automatic differentiation for optimizing circuit parameters is familiar to machine learning (ML). While the results bear interesting promise for an era when quantum machines are more readily accessible, if one can achieve similar results through non-quantum methods then there may be a more near-term advantage available to practitioners. To this end, the nature of this work is to investigate the utilization of stochastic methods inspired by a variational quantum version of the long short-term memory (LSTM) model in an attempt to approach the reported successes in performance and rapid convergence. By analyzing the performance of classical, stochastic, and quantum methods, this work aims to elucidate if it is possible to achieve some of QML's major reported benefits on classical machines by incorporating aspects of its stochasticity.

Facial Expression Recognition at the Edge: CPU vs GPU vs VPU vs TPU

Facial Expression Recognition (FER) plays an important role in human-computer interactions and is used in a wide range of applications. Convolutional Neural Networks (CNN) have shown promise in their ability to classify human facial expressions, however, large CNNs are not well-suited to be implemented on resource- and energy-constrained IoT devices. In this work, we present a hierarchical framework for developing and optimizing hardware-aware CNNs tuned for deployment at the edge. We perform a comprehensive analysis across various edge AI accelerators including NVIDIA Jetson Nano, Intel Neural Compute Stick, and Coral TPU. Using the proposed strategy, we achieved a peak accuracy of 99.49% when testing on the CK+ facial expression recognition dataset. Additionally, we achieved a minimum inference latency of 0.39 milliseconds and a minimum power consumption of 0.52 Watts.

IMAC-Sim: A Circuit-level Simulator For In-Memory Analog Computing Architectures

With the increased attention to memristive-based in-memory analog computing (IMAC) architectures as an alternative for energy-hungry computer systems for machine learning applications, a tool that enables exploring their device- and circuit-level design space can significantly boost the research and development in this area. Thus, in this paper, we develop IMAC-Sim, a circuit-level simulator for the design space exploration of IMAC architectures. IMAC-Sim is a Python-based simulation framework, which creates the SPICE netlist of the IMAC circuit based on various device- and circuit-level hyperparameters selected by the user, and automatically evaluates the accuracy, power consumption, and latency of the developed circuit using a user-specified dataset. Moreover, IMAC-Sim simulates the interconnect parasitic resistance and capacitance in the IMAC architectures and is also equipped with horizontal and vertical partitioning techniques to surmount these reliability challenges. IMAC-Sim is a flexible tool that supports a broad range of device- and circuit-level hyperparameters. In this paper, we perform controlled experiments to exhibit some of the important capabilities of the IMAC-Sim, while the entirety of its features is available for researchers via an open-source tool.

Heterogeneous Integration of In-Memory Analog Computing Architectures with Tensor Processing Units

Tensor processing units (TPUs), specialized hardware accelerators for machine learning tasks, have shown significant performance improvements when executing convolutional layers in convolutional neural networks (CNNs). However, they struggle to maintain the same efficiency in fully connected (FC) layers, leading to suboptimal hardware utilization. In-memory analog computing (IMAC) architectures, on the other hand, have demonstrated notable speedup in executing FC layers. This paper introduces a novel, heterogeneous, mixed-signal, and mixed-precision architecture that integrates an IMAC unit with an edge TPU to enhance mobile CNN performance. To leverage the strengths of TPUs for convolutional layers and IMAC circuits for dense layers, we propose a unified learning algorithm that incorporates mixed-precision training techniques to mitigate potential accuracy drops when deploying models on the TPU-IMAC architecture. The simulations demonstrate that the TPU-IMAC configuration achieves up to $2.59\times$ performance improvements, and $88\%$ memory reductions compared to conventional TPU architectures for various CNN models while maintaining comparable accuracy. The TPU-IMAC architecture shows potential for various applications where energy efficiency and high performance are essential, such as edge computing and real-time processing in mobile devices. The unified training algorithm and the integration of IMAC and TPU architectures contribute to the potential impact of this research on the broader machine learning landscape.

Energy-Efficient Deployment of Machine Learning Workloads on Neuromorphic Hardware

As the technology industry is moving towards implementing tasks such as natural language processing, path planning, image classification, and more on smaller edge computing devices, the demand for more efficient implementations of algorithms and hardware accelerators has become a significant area of research. In recent years, several edge deep learning hardware accelerators have been released that specifically focus on reducing the power and area consumed by deep neural networks (DNNs). On the other hand, spiking neural networks (SNNs) which operate on discrete time-series data, have been shown to achieve substantial power reductions over even the aforementioned edge DNN accelerators when deployed on specialized neuromorphic event-based/asynchronous hardware. While neuromorphic hardware has demonstrated great potential for accelerating deep learning tasks at the edge, the current space of algorithms and hardware is limited and still in rather early development. Thus, many hybrid approaches have been proposed which aim to convert pre-trained DNNs into SNNs. In this work, we provide a general guide to converting pre-trained DNNs into SNNs while also presenting techniques to improve the deployment of converted SNNs on neuromorphic hardware with respect to latency, power, and energy. Our experimental results show that when compared against the Intel Neural Compute Stick 2, Intel's neuromorphic processor, Loihi, consumes up to 27x less power and 5x less energy in the tested image classification tasks by using our SNN improvement techniques.