Edge inference techniques partition and distribute Deep Neural Network (DNN) inference tasks among multiple edge nodes for low latency inference, without considering the core-level heterogeneity of edge nodes. Further, default DNN inference frameworks also do not fully utilize the resources of heterogeneous edge nodes, resulting in higher inference latency. In this work, we propose a hierarchical DNN partitioning strategy (HiDP) for distributed inference on heterogeneous edge nodes. Our strategy hierarchically partitions DNN workloads at both global and local levels by considering the core-level heterogeneity of edge nodes. We evaluated our proposed HiDP strategy against relevant distributed inference techniques over widely used DNN models on commercial edge devices. On average our strategy achieved 38% lower latency, 46% lower energy, and 56% higher throughput in comparison with other relevant approaches,

Compound AI (cAI) systems chain multiple AI models to solve complex problems. cAI systems are typically composed of deep neural networks (DNNs), transformers, and large language models (LLMs), exhibiting a high degree of computational diversity and dynamic workload variation. Deploying cAI services on mobile edge platforms poses a significant challenge in scheduling concurrent DNN-transformer inference tasks, which arrive dynamically in an unknown sequence. Existing mobile edge AI inference strategies manage multi-DNN or transformer-only workloads, relying on design-time profiling, and cannot handle concurrent inference of DNNs and transformers required by cAI systems. In this work, we address the challenge of scheduling cAI systems on heterogeneous mobile edge platforms. We present Twill, a run-time framework to handle concurrent inference requests of cAI workloads through task affinity-aware cluster mapping and migration, priority-aware task freezing/unfreezing, and Dynamic Voltage/Frequency Scaling (DVFS), while minimizing inference latency within power budgets. We implement and deploy our Twill framework on the Nvidia Jetson Orin NX platform. We evaluate Twill against state-of-the-art edge AI inference techniques over contemporary DNNs and LLMs, reducing inference latency by 54% on average, while honoring power budgets.

Run-time resource management (RTM) of multi-programmed workloads on heterogeneous multi-core platforms is challenging due to (i) fixed power budget of the device, (ii) variable performance requirements of the workloads, and (iii) unknown arrival of the applications. Existing RTM solutions lack power-performance coordination, resulting in performance degradation during power actuation or power violations during performance provisioning. Exploiting inherent error-resilience of the applications can address the performance loss incurred in power actuation, by combining run-time approximation with traditional power knobs (including Dynamic Voltage/Frequency Scaling, Task Migration, Degree of Parallelism, and CPU Quota ). In this work, we present an accuracy-aware resource management framework that jointly actuates run-time approximation and traditional power knobs for efficient power-performance management of multi-programmed and multi-threaded workloads running on heterogeneous mobile platforms. Our strategy configures the accuracy of the applications at run-time to exploit accuracy-performance trade-offs, by considering system-wide power-performance dynamics. We use heuristic estimation models to jointly enforce accuracy configuration and traditional power knobs settings at run-time. We evaluated our framework on real-world embedded mobile platforms, including Odroid XU3 and Asus Tinker Edge R boards to demonstrate the efficiency of our proposed approach across multiple workload scenarios. Our approach achieved 25% lower performance violations against the state-of-the-art run-time resource management policies at the cost of 2.2% accuracy loss across six applications.

DNN inference can be accelerated by distributing the workload among a cluster of collaborative edge nodes. Heterogeneity among edge devices and accuracy-performance trade-offs of DNN models present a complex exploration space while catering to the inference performance requirements. In this work, we propose adaptive workload distribution for DNN inference, jointly considering node-level heterogeneity of edge devices, and application-specific accuracy and performance requirements. Our proposed approach combinatorially optimizes heterogeneity-aware workload partitioning and dynamic accuracy configuration of DNN models to ensure performance and accuracy guarantees. We tested our approach on an edge cluster of Odroid XU4, Raspberry Pi4, and Jetson Nano boards and achieved an average gain of 41.52% in performance and 5.2% in output accuracy as compared to state-of-the-art workload distribution strategies.

There is an increasing demand to run DNN applications on edge platforms for low-latency inference. Executing multi-DNN workloads with diverse compute and latency requirements on resource-constrained heterogeneous edge platforms poses a significant scheduling challenge. In this work, we present Tango framework for orchestrating multi-DNN inference on heterogeneous edge platforms. Our approach uses a Proximal Policy-based Reinforcement Learning agent to jointly optimize cluster selection, accuracy configuration, and frequency scaling to minimize inference latency with a tolerable accuracy loss. We implemented the proposed Tango framework as a portable middleware and deployed it on real hardware of the Jetson TX edge platform. Our evaluation against relevant multi-DNN scheduling strategies demonstrates 61 % lower latency and 48.4 % lower energy consumption at a maximum accuracy loss of 1.59 %.

Recent advancements in hardware and software systems have been driven by the deployment of emerging smart health and mobility applications. These developments have modernized the traditional approaches by replacing conventional computing systems with cyber-physical and intelligent systems combining the Internet of Things (IoT) with Edge Artificial Intelligence. Despite the many advantages and opportunities of these systems within various application domains, the scarcity of energy, extensive computing needs, and limited communication must be considered when orchestrating their deployment. Inducing savings in these directions is central to the Approximate Computing (AxC) paradigm, in which the accuracy of some operations is traded off with energy, latency, and/or communication reductions. Unfortunately, the dynamics of the environments in which AxC-equipped IoT systems operate have been paid little attention. We bridge this gap by surveying adaptive AxC techniques applied to three emerging application domains, namely autonomous driving, smart sensing and wearables, and positioning, paying special attention to hardware acceleration. We discuss the challenges of such applications, how adaptive AxC can aid their deployment, and which savings it can bring based on traits of the data and devices involved. Insights arising thereof may serve as inspiration to researchers, engineers, and students active within the considered domains.