Touch-sensitive stretchable electronic skins (e-skins) hold promise for soft robots, prosthetics, bio-mimetics, and bio-sensors. However, a long-standing challenge has been the interference of stretching in pressure readings. Addressing this, we introduce an intrinsically stretchable hybrid response pressure sensor (SHRPS) composed of a laminate featuring a barely conductive porous nanocomposite and an ultrathin dielectric layer situated between two stretchable electrodes. The combined piezoresistive and piezocapacitive responses of the SHRPS enable ultrahigh pressure sensitivity while effectively negating stretch-induced interference. Our findings are underpinned by an experimentally validated electromechanical model. In practical applications, SHRPS mounted on inflatable probes demonstrated safe and precise palpation on the human wrist and conformable and firm gripping of contoured objects. The debut of SHRPS promises to significantly expand the versatile applications of e-skins.
@article{ha24shrps,title={Stretchable hybrid response pressure sensors},journal={Matter},volume={7},number={5},pages={1895-1908},year={2024},issn={2590-2385},doi={https://doi.org/10.1016/j.matt.2024.04.009},url={https://www.sciencedirect.com/science/article/pii/S2590238524001644},author={Ha, Kyoung-Ho and Li, Zhengjie and Kim, Sangjun and Huh, Heeyong and Wang, Zheliang and Shi, Hongyang and Block, Charles and Bhattacharya, Sarnab and Lu, Nanshu},keywords={stretchable pressure sensor, electronic skin, decoupling of pressure and stretch, inflatable soft robots, human-computer interaction, heartbeat palpation}}
Sparse matrix dense matrix multiplication (SpMM) is commonly used in applications ranging from scientific computing to graph neural networks. Typically, when SpMM is executed in a distributed platform, communication costs dominate. Such costs depend on how communication is scheduled. If it is scheduled in a sparsity-unaware manner, such as with collectives, execution is often inefficient due to unnecessary data transfers. On the other hand, if communication is scheduled in a fine-grained sparsity-aware manner, communicating only the necessary data, execution can also be inefficient due to high software overhead.We observe that individual sparse matrices often contain regions that are denser and regions that are sparser. Based on this observation, we develop a model that partitions communication into sparsity-unaware and sparsity-aware components. Leveraging the partition, we develop a new algorithm that performs collective communication for the denser regions, and fine-grained, one-sided communication for the sparser regions. We call the algorithm Two-Face. We show that Two-Face attains an average speedup of 2.11x over prior work when evaluated on a 4096-core supercomputer. Additionally, Two-Face scales well with the machine size.
@inproceedings{block24twoface,author={Block, Charles and Gerogiannis, Gerasimos and Mendis, Charith and Azad, Ariful and Torrellas, Josep},title={Two-Face: Combining Collective and One-Sided Communication for Efficient Distributed SpMM},year={2024},isbn={9798400703850},publisher={Association for Computing Machinery},address={New York, NY, USA},url={https://doi.org/10.1145/3620665.3640427},doi={10.1145/3620665.3640427},booktitle={Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2},pages={1200–1217},numpages={18},keywords={high-performance computing, distributed algorithms, sparse matrices, SpMM},location={La Jolla, CA, USA},series={ASPLOS '24}}