Tactile sensing is pivotal for enabling effective human-robot interaction, especially in unstructured environments. This work introduces an innovative bioinspired soft robotic finger endowed with shape-adaptive and multi-modal tactile perception capabilities, drawing inspiration from diverse biological tactile sensing modalities. Through an advanced Fin Ray structure, the soft finger features tactile whiskers on its fingertips, facilitating perception of obstacle orientation, fingertip pressure, surface roughness, and grasping ball size. Leveraging distributed optical fiber sensing technology, we develop a sophisticated multi-point, multi-modal tactile perception neural network tailored for the soft finger. Meticulous integration via advanced 3D printing and silicone coating techniques seamlessly embeds optical fiber sensors within the soft robotic finger, creating an intelligent perception-capable bioinspired mechanical system. Experimental validation confirms the soft robotic finger’s sensitive and precise force perception and curvature recognition abilities, achieving accuracies of up to 100%. In summary, our bioinspired robotic finger holds significant promise for applications in intelligent sensing, non-destructive grasping, and fruit classification within unstructured environments, thus advancing the field of robotics and human-robot interaction.

As the Internet of Things (IoTs) increasingly combines AI technology, it is a trend to deploy neural network algorithms at edges and make IoT devices more intelligent than ever. Moreover, energy-harvesting technology-based IoT devices have shown the advantages of green and low-carbon economy, convenient maintenance, and theoretically infinite lifetime, and so on. However, the harvested energy is often unstable, resulting in low performance due to the fact that a fixed load cannot sufficiently utilize the harvested energy. To address this problem, recent works focusing on ReRAM-based convolutional neural networks (CNN) accelerators under harvested energy have proposed hardware/software optimizations. However, those works have overlooked the mismatch between the power requirement of different CNN layers and the variation of harvested power. Motivated by the above observation, this article proposes a novel strategy, called REC , that retimes convolutional layers of CNN inferences to improve the performance and energy efficiency of energy harvesting ReRAM-based accelerators. Specifically, at the offline stage, REC defines different power levels to fit the power requirements of different convolutional layers. At runtime, instead of sequentially executing the convolutional layers of an inference one by one, REC retimes the execution timeframe of different convolutional layers so as to accommodate different CNN layers to the changing power inputs. What is more, REC provides a parallel strategy to fully utilize very high power inputs. Moreover, a case study is presented to show that REC is effective to improve the real-time accomplishment of periodical critical inferences because REC provides an opportunity for critical inferences to preempt the process window with a high power supply. Our experimental results show that the proposed REC scheme achieves an average performance improvement of 6.1× (up to 16.5×) compared to the traditional strategy without the REC idea. The case study results show that the REC scheme can significantly improve the success rate of periodical critical inferences’ real-time accomplishment.

Checkpointing is a key technique to guarantee execution correctness and ensure progress forwarding in energy harvesting systems. However, checkpointing itself introduces system overhead due to extra operations of data movements between volatile memory and nonvolatile memory. Moreover, execution rollback to the latest checkpoint under a power failure can cancel some obtained progress and this waste is highly correlated to the latest checkpoint interval. These two kinds of overhead can be quite high if the checkpoint interval setting mismatches the power input characteristic. Unlike previous checkpointing schemes emphasizing on optimizing data copy overhead, this paper further takes into account the characteristic of input power sources and proposes a resilient checkpointing scheme, ResCheck, which can capture the power input changes and thus accordingly mitigate checkpoint overhead and rollback cost. The proposed ResCheck, directed by a lightweight neural network-based power level predictor, is capable of adjusting the checkpoint intervals to fit different power levels at runtime. In this way, an energy harvesting system equipped with ResCheck can achieve both fewer checkpoint number and lower execution rollback punishment. Our experimental results show that ResCheck can reduce an average checkpoint number of 24.4% and 14.6% over the conventional periodic checkpointing scheme and the state-of-the-art iCheck scheme respectively. Meanwhile, ResCheck improves average performance as well as energy efficiency by more than three times compared to iCheck.

Energy consumption has been a big challenge for electronic devices, particularly for battery-powered Internet of Things (IoT) equipment. To address such a challenge, on the one hand, low-power electronic design methodologies and novel power management techniques have been proposed, such as nonvolatile memories and instantly on/off systems; on the other hand, the energy harvesting technology by collecting signals from human activity or the environment has attracted widespread attention in the IoT area. However, the system with self-powered energy harvesting may suffer frequent energy failures or fluctuating energy conditions, which degrade system reliability and user experience. Therefore, how to make the system under unreliable power inputs operate correctly and efficiently is one of the most critical issues for energy harvesting technology. In this article, we built an instantly on/off system based on nonvolatile STT-MRAM for IoT applications, which can instantly power on/off under different conditions of the harvested energy. The system powers on and operates normally when the harvested energy is enough (over the preset threshold); otherwise, the system powers off and stores the operational data back to the nonvolatile STT-MRAM. We described implementations of the hardware/software co-designed architecture (with image acquisition as an example) based on the commercialized 32 MB STT-MRAM, and we experimentally demonstrated the system functionality and efficiency under five typical energy harvesting scenarios, including radio frequency, thermal, solar, piezoelectric, and WIFI. Our experimental results show that the power consumption and data restore time were reduced by 15.1% and 714 times, respectively, in comparison with the DRAM-based counterpart.

As the Internet of Things (IoTs) increasingly combines AI technology, it is a trend to deploy neural network algorithms at edges and make IoT devices more intelligent than ever. Moreover, the energy harvesting technology-based IoT devices have shown the advantages of green economy, convenient maintenance, and theoretically infinite lifetime, etc. However, the harvested energy is often unstable, resulting in low performance due to the fact that a fixed load can't sufficiently utilize the harvested energy. To address this problem, recent works focusing on ReRAM-based convolutional neural networks (CNN) accelerators under harvested energy have proposed hardware/software optimizations. However, those works have overlooked the mismatch between the power requirement of different CNN layers and the variation of harvested power.

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.