Energy Harvesting-assisted Ultra-Low-Power Processing-in-Memory Accelerator for ML Applications

The proliferation of Internet of Things (IoT) and edge computing devices has become an essential aspect of our daily routines. Particularly, the rise of wearable technology like smartwatches, health trackers, and smart glasses has contributed significantly to their popularity. These gadgets are equipped with diverse sensors that enable researchers and manufacturers to collect user data. Subsequently, this data undergoes processing through on-device Machine Learning (ML) algorithms, enhancing user interactions. However, implementing ML algorithms on these compact IoTs and edge devices consumes substantial power and energy. It’s crucial to recognize that these devices operate within strict energy and power constraints. Thus, optimizing battery usage is paramount for prolonging a device’s lifespan. Therefore, we propose a Processing-In-Memory (PIM) architecture utilizing Look-up-Table (LUT) based processing for improved performance and energy efficiency. To further enhance energy efficiency in this work we introduce a framework that efficiently utilizes kinetic energy harvesting to intermittently support ML computations/tasks, thereby alleviating the load on the device’s built-in battery. By offloading ML computations to the PIM architecture, the framework reduces the reliance on the device’s internal battery power, optimizing the use of harvested kinetic energy and extending battery life. Furthermore, PIM architecture facilitates seamless integration of harvested kinetic energy, ensuring efficient ML computations with minimal energy consumption. This integrated approach presents a compelling solution for energy management in IoT and edge-based applications, as evidenced by experiments and analysis showing significant reductions in overall energy usage. We evaluated the proposed Energy Harvesting-assisted PIM architecture on various CNN architectures, such as LeNet, AlexNet, ResNet -18, -34, -50.