Search for: All records

There is an ongoing trend to increasingly offload inference tasks, such as CNNs, to edge devices in many IoT scenarios. As energy harvesting is an attractive IoT power source, recent ReRAM-based CNN accelerators have been designed for operation on harvested energy. When addressing the instability problems of harvested energy, prior optimization techniques often assume that the load is fixed, overlooking the close interactions among input power, computational load, and circuit efficiency, or adapt the dynamic load to match the just-in-time incoming power under a simple harvesting architecture with no intermediate energy storage. Targeting a more efficient harvesting architecture equipped with both energy storage and energy delivery modules, this paper is the first effort to target whole system, end-to-end efficiency for an energy harvesting ReRAM-based accelerator. First, we model the relationships among ReRAM load power, DC-DC converter efficiency, and power failure overhead. Then, a maximum computation progress tracking scheme ( MaxTracker ) is proposed to achieve a joint optimization of the whole system by tuning the load power of the ReRAM-based accelerator. Specifically, MaxTracker accommodates both continuous and intermittent computing schemes and provides dynamic ReRAM load according to harvesting scenarios. We evaluate MaxTracker over four input power scenarios, and the experimental results show average speedups of 38.4%/40.3% (up to 51.3%/84.4%), over a full activation scheme (with energy storage) and order-of-magnitude speedups over the recently proposed (energy storage-less) ResiRCA technique. Furthermore, we also explore MaxTracker in combination with the Capybara reconfigurable capacitor approach to offer more flexible tuners and thus further boost the system performance. 

There is an increasing demand for performing machine learning tasks, such as human activity recognition (HAR) on emerging ultra-low-power internet of things (IoT) platforms. Recent works show substantial efficiency boosts from performing inference tasks directly on the IoT nodes rather than merely transmitting raw sensor data. However, the computation and power demands of deep neural network (DNN) based inference pose significant challenges when executed on the nodes of an energy-harvesting wireless sensor network (EH-WSN). Moreover, managing inferences requiring responses from multiple energy-harvesting nodes imposes challenges at the system level in addition to the constraints at each node. This paper presents a novel scheduling policy along with an adaptive ensemble learner to efficiently perform HAR on a distributed energy-harvesting body area network. Our proposed policy, Origin, strategically ensures efficient and accurate individual inference execution at each sensor node by using a novel activity-aware scheduling approach. It also leverages the continuous nature of human activity when coordinating and aggregating results from all the sensor nodes to improve final classification accuracy. Further, Origin proposes an adaptive ensemble learner to personalize the optimizations based on each individual user. Experimental results using two different HAR data-sets show Origin, while running on harvested energy, to be at least 2.5% more accurate than a classical battery-powered energy aware HAR classifier continuously operating at the same average power. 

With the end of Dennard scaling, power constraints have led to increasing compute specialization in the form of differently specialized accelerators integrated at various levels of the general-purpose system hierarchy. The result is that the most common general-purpose computing platform is now a heterogeneous mix of architectures even within a single die. Consequently, mapping application code regions into available execution engines has become a challenge due to different interfaces and increased software complexity. At the same time, the energy costs of data movement have become increasingly dominant relative to computation energy. This has inspired a move towards data-centric systems, where computation is brought to data, in contrast to traditional processing-centric models. However, enabling compute nearer memory entails its own challenges, including the interactions between distance-specialization and compute-specialization. The granularity of any offload to near(er) memory logic would impact the potential data transmission reduction, as smaller offloads will not be able to amortize the transmission costs of invocation and data return, while very large offloads can only be mapped onto logic that can support all of the necessary operations within kernel-scale codes, which exacerbates both area and power constraints. For better energy efficiency, each set of related operations should be mapped onto the execution engine that, among those capable of running the set of operations, best balances the data movement and the degree of compute specialization of that engine for this code. Further, this offload should proceed in a decentralized way that keeps both the data and control movement low for all transitions among engines and transmissions of operands and results. To enable such a decentralized offload model, we propose an architecture interface that enables a common offload model for accelerators across the memory hierarchy and a tool chain to automatically identify (in a distance-aware fashion) and map profitable code regions on specialized execution engines. We evaluate the proposed architecture for a wide range of workloads and show energy reduction compared to an energy-efficient in-order core. We also demonstrate better area efficiency compared to kernel-scale offloads. 

Many recent works have shown substantial efficiency boosts from performing inference tasks on Internet of Things (IoT) nodes rather than merely transmitting raw sensor data. However, such tasks, e.g., convolutional neural networks (CNNs), are very compute intensive. They are therefore challenging to complete at sensing-matched latencies in ultra-low-power and energy-harvesting IoT nodes. ReRAM crossbar-based accelerators (RCAs) are an ideal candidate to perform the dominant multiplication-and-accumulation (MAC) operations in CNNs efficiently, but conventional, performance-oriented RCAs, while energy-efficient, are power hungry and ill-optimized for the intermittent and unstable power supply of energy-harvesting IoT nodes. This paper presents the ResiRCA architecture that integrates a new, lightweight, and configurable RCA suitable for energy harvesting environments as an opportunistically executing augmentation to a baseline sense-and-transmit battery-powered IoT node. To maximize ResiRCA throughput under different power levels, we develop the ResiSchedule approach for dynamic RCA reconfiguration. The proposed approach uses loop tiling-based computation decomposition, model duplication within the RCA, and inter-layer pipelining to reduce RCA activation thresholds and more closely track execution costs with dynamic power income. Experimental results show that ResiRCA together with ResiSchedule achieve average speedups and energy efficiency improvements of 8× and 14× respectively compared to a baseline RCA with intermittency-unaware scheduling.