Autonomous Things (AuT) refers to a collection of self-sufficient tiny devices capable of performing intelligent computations. Looking ahead, AuT promises to enable ubiquitous deployment of intelligence on many emerging consumer electronics and mission-critical infrastructures. Nevertheless, there is an important research gap to date: architecting efficient AuT systems requires both energy autonomy (EA) and inference autonomy (IA). In other words, practical AuT application scenarios necessitate tailored architectures with significantly expanded inference performance and more efficient use of energy.We present CHRYSALIS, a novel automated EA/IA co-design methodology for autonomous things. It aims to guide the transition from a traditional EA-only and IA-only design approach to a truly AuT-oriented architecture design. To fully understand the interrelationship between the EA domain and the IA domain, CHRYSALIS first introduces an architectural modeling framework encompassing every key AuT module involving energy harvesting, intermittent execution, and accelerator control. Based on the holistic system model, we design an intelligent architecture generation tool that can help find the ideal design for targeted AuT scenarios adhering to different SWaP (Size, Weight and Power) constraints. To validate our work, we use CHRYSALIS for fast construction and exploration of efficient AuT design and pre-RTL design in representative AuT scenarios. Extensive evaluation shows that CHRYSALIS outperforms state-of-the-art designs and our proposed technique shows 56.4% better performance on average. We believe that the methodology and tools developed in this paper will foster the development of more performant and practical architectures in the upcoming AuT era.

With the rapid progress and widespread adoption of AI technology, integrating powerful DNN models into AIoT devices in close proximity to users has become increasingly appealing. However, a significant challenge is that it is not easy to achieve the stringent Quality of Service (QoS) standards, especially in terms of real-time latency, demanded by the computationally intensive DNN workloads in energy-limited AIoT environments. To address this challenge, prior research has focused on per-layer power management techniques, which aggressively exploit the unique energy and performance relationships exhibited by each layer of the DNN at an exceedingly fine-grained control granularity. In this study, we identify the limitations of the existing per-layer DVFS mechanisms. They severely overlook the significant DVFS overhead caused by the excessively fine-grained control which can introduce complexity to power management in practical scenarios, consequently deteriorating QoS. To mitigate these challenges, we propose CPM, a Cross-layer Power Mangement facility which automatically modularizes different DNN layers and performs the best DVFS policy, thereby enhancing QoS by ensuring lower latency in real-time AIoT systems. Additionally, we integrate CPM into mainstream commercial AIoT boards and systems to validate its effusiveness. The results show that CPM can reduce the execution latency by up to 45.76% while improving the energy efficiency by up to 31.58% of real AIoT systems compared to SOTA per-layer power management methods.