eHealth systems deliver critical digital healthcare and wellness services for users by continuously monitoring physiological and contextual data. eHealth applications use multi-modal machine learning kernels to analyze data from different sensor modalities and automate decision-making. Noisy inputs and motion artifacts during sensory data acquisition affect the i) prediction accuracy and resilience of eHealth services and ii) energy efficiency in processing garbage data. Monitoring raw sensory inputs to identify and drop data and features from noisy modalities can improve prediction accuracy and energy efficiency. We propose a closed-loop monitoring and control framework for multi-modal eHealth applications, AMSER, that can mitigate garbage-in garbage-out by i) monitoring input modalities, ii) analyzing raw input to selectively drop noisy data and features, and iii) choosing appropriate machine learning models that fit the configured data and feature vector - to improve prediction accuracy and energy efficiency. We evaluate our AMSER approach using multi-modal eHealth applications of pain assessment and stress monitoring over different levels and types of noisy components incurred via different sensor modalities. Our approach achieves up to 22% improvement in prediction accuracy and 5.6× energy consumption reduction in the sensing phase against the state-of-the-art multi-modal monitoring application.

Health and wellness applications increasingly rely on machine learning techniques to learn end-user physiological and behavioral patterns in everyday settings, posing two key challenges: inability to perform on-device online learning for resource-constrained wearables, and learning algorithms that support privacy-preserving personalization. We exploit a Hyperdimensional computing (HDC) solution for wearable devices that offers flexibility, high efficiency, and performance while enabling on-device personalization and privacy protection. We evaluate the efficacy of our approach using three case studies and show that our system improves performance of training by up to 35.8x compared with the state-of-the-art while offering a comparable accuracy.

In this paper, we present Hyperdimensional Hybrid Learning (HDHL), which combines model-free and model-based Reinforcement Learning, to effectively reduce the computational cost and environment interaction for optimizing an intelligent cloud service. We first show that Hyperdimensional Q-Learning (QHD), the state-of-the-art Hyperdimensional Computing value-based Reinforcement Learning algorithm, is computationally faster than the Deep Q-Network (DQN) for this task. In addition, we demonstrate how HDHL reduces the number of environment interactions by 4.8× to learn the near optimal configuration. Our evaluation shows that HDHL is computationally more efficient than both Q-Learning algorithms, with the total time being reduced by 21.0× compared to DQN and 16.5× compared to QHD.

Internet of Things (IoT) paradigm raises challenges for devising efficient strategies that offload applications to the fog or the cloud layer while ensuring the optimal response time for a service. Traditional computation offloading policies assume the response time is only dominated by the execution time. However, the response time is a function of many factors including contextual parameters and application characteristics that can change over time. For the computation offloading problem, the majority of existing literature presents efficient solutions considering a limited number of parameters (e.g., computation capacity and network bandwidth) neglecting the effect of the application characteristics and dataflow configuration. In this paper, we explore the impact of the computation offloading on total application response time in three-layer IoT systems considering more realistic parameters, e.g., application characteristics, system complexity, communication cost, and dataflow configuration. This paper also highlights the impact of a new application characteristic parameter defined as Output–Input Data Generation (OIDG) ratio and dataflow configuration on the system behavior. In addition, we present a proof-of-concept end-to-end dynamic computation offloading technique, implemented in a real hardware setup, that observes the aforementioned parameters to perform real-time decision-making.

Deep-learning-based intelligent services have become prevalent in cyber-physical applications, including smart cities and health-care. Deploying deep-learning-based intelligence near the end-user enhances privacy protection, responsiveness, and reliability. Resource-constrained end-devices must be carefully managed to meet the latency and energy requirements of computationally intensive deep learning services. Collaborative end-edge-cloud computing for deep learning provides a range of performance and efficiency that can address application requirements through computation offloading. The decision to offload computation is a communication-computation co-optimization problem that varies with both system parameters (e.g., network condition) and workload characteristics (e.g., inputs). However, deep learning model optimization provides another source of tradeoff between latency and model accuracy. An end-to-end decision-making solution that considers such computation-communication problem is required to synergistically find the optimal offloading policy and model for deep learning services. To this end, we propose a reinforcement-learning-based computation offloading solution that learns optimal offloading policy considering deep learning model selection techniques to minimize response time while providing sufficient accuracy. We demonstrate the effectiveness of our solution for edge devices in an end-edge-cloud system and evaluate with a real-setup implementation using multiple AWS and ARM core configurations. Our solution provides 35% speedup in the average response time compared to the state-of-the-art with less than 0.9% accuracy reduction, demonstrating the promise of our online learning framework for orchestrating DL inference in end-edge-cloud systems.

Quantum computers threaten to break public-key cryptography schemes such as DSA and ECDSA in polynomial time, which poses an imminent threat to secure signal processing. Ring learning with error (RLWE) lattice-based cryptography (LBC) is one of the most promising families of post-quantum cryptography (PQC) schemes in terms of efficiency and versatility. Two conventional methods to compute polynomial multiplication, the most compute-intensive routine in the RLWE schemes, are convolutions and Number Theoretic Transform (NTT).In this work, we explore the energy efficiency of polynomial multiplier using systolic architecture for the first time. As an early exploration, we design two high-throughput systolic array polynomial multipliers, including NTT-based and convolution-based, and compare them to our low-cost sequential (non-systolic) NTT-based multiplier. Our sequential NTT-based multiplier achieves 3x speedup over the state-of-the-art FGPA implementation of the polynomial multiplier in the NewHope-Simple key exchange mechanism on a low-cost Artix7 FPGA. When synthesized on a Zynq UltraScale+ FPGA, the NTT-based systolic and convolution-based systolic designs achieve on average 1.7x and 7.5x speedup over our sequential NTT-based multiplier respectively, which can lead to generating over 2x more signatures per second by CRYSTALS-Dilithium, a PQC digital signature scheme. These explorations help designers select the right PQC implementations for making future signal processing applications quantum-resistant.

As recent neural networks are being improved to be more accurate, their model's size is exponentially growing. Thus, a huge number of parameters requires to be loaded and stored from/in memory hierarchy and computed in processors to perform training or inference phase of neural network processing. Increasing the number of parameters causes a big challenge for real-time deployment since the memory bandwidth improvement's trend cannot keep up with models' complexity growing trend. Although some operations in neural networks processing are computational intensive such as convolutional layer computing, computing dense layers face with memory bandwidth bottleneck. To address the issue, the paper has proposed Partition Pruning for dense layers to reduce the required parameters while taking into consideration parallelization. We evaluated the performance and energy consumption of parallel inference of partitioned models, which showed a 7.72× speedup of performance and a 2.73× reduction in the energy used for computing pruned fully connected layers in TinyVGG16 model in comparison to running the unpruned model on a single accelerator. Besides, our method showed a limited reduction in accuracy while partitioning fully connected layers.

In the era of Fog computing where one can decide to compute certain time-critical tasks at the edge of the network, designers often encounter a question whether the sensor layer provides the optimal response time for a service, or the Fog layer, or their combination. In this context, minimizing the total response time using computation migration is a communication-computation co-optimization problem as the response time does not depend only on the computational capacity of each side. In this paper, we aim at investigating this question and addressing it in certain situations. We formulate this question as a static or dynamic computation migration problem depending on whether certain communication and computation characteristics of the underlying system is known at design-time or not. We first propose a static approach to find the optimal computation migration strategy using models known at design-time. We then make a more realistic assumption that several sources of variation can affect the system's response latency (e.g., the change in computation time, bandwidth, transmission channel reliability, etc.), and propose a dynamic computation migration approach which can adaptively identify the latency optimal computation layer at runtime. We evaluate our solution using a case-study of artificial neural network based arrhythmia classification using a simulation environment as well as a real test-bed.