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Serverless computing is an increasingly attractive paradigm in the cloud due to its ease of use and fine-grained pay-for-what-you-use billing. However, serverless computing poses new challenges to system design due to its short-lived function execution model. Our detailed analysis reveals that memory management is responsible for a major amount of function execution cycles. This is because functions pay the full critical-path costs of memory management in both userspace and the operating system without the opportunity to amortize these costs over their short lifetimes. To address this problem, we propose Memento, a new hardware-centric memory management design based upon our insights that memory allocations in serverless functions are typically small, and either quickly freed after allocation or freed when the function exits. Memento alleviates the overheads of serverless memory management by introducing two key mechanisms: (i) a hardware object allocator that performs in-cache memory allocation and free operations based on arenas, and (ii) a hardware page allocator that manages a small pool of physical pages used to replenish arenas of the object allocator. Together these mechanisms alleviate memory management overheads and bypass costly userspace and kernel operations. Memento naturally integrates with existing software stacks through a set of ISA extensions that enable seamless integration with multiple languages runtimes. Finally, Memento leverages the newly exposed memory allocation semantics in hardware to introduce a main memory bypass mechanism and avoid unnecessary DRAM accesses for newly allocated objects. We evaluate Memento with full-system simulations across a diverse set of containerized serverless workloads and language runtimes. The results show that Memento achieves function execution speedups ranging between 8–28% and 16% on average. Furthermore, Memento hardware allocators and main memory bypass mechanisms drastically reduce main memory traffic by 30% on average. The combined effects of Memento reduce the pricing cost of function execution by 29%. Finally, we demonstrate the applicability of Memento beyond functions, to major serverless platform operations and long-running data processing applications. 

The chiral induced spin selectivity (CISS) effect, in which the structural chirality of a material determines the preference for the transmission of electrons with one spin orientation over that of the other, is emerging as a design principle for creating next-generation spintronic devices. CISS implies that the spin preference of chiral structures persists upon injection of pure spin currents and can act as a spin analyzer without the need for a ferromagnet. Here, we report an anomalous spin current absorption in chiral metal oxides that manifests a colossal anisotropic nonlocal Gilbert damping with a maximum-to-minimum ratio of up to 1000%. A twofold symmetry of the damping is shown to result from differential spin transmission and backscattering that arise from chirality-induced spin splitting along the chiral axis. These studies reveal the rich interplay of chirality and spin dynamics and identify how chiral materials can be implemented to direct the transport of spin current. 

Wearable devices like smartwatches and smart wristbands have gained substantial popularity in recent years. However, their small interfaces create inconvenience and limit computing functionality. To fill this gap, we propose ViWatch, which enables robust finger interactions under deployment variations, and relies on a single IMU sensor that is ubiquitous in COTS smartwatches. To this end, we design an unsupervised Siamese adversarial learning method. We built a real-time system on commodity smartwatches and tested it with over one hundred volunteers. Results show that the system accuracy is about 97% over a week. In addition, it is resistant to deployment variations such as different hand shapes, finger activity strengths, and smartwatch positions on the wrist. We also developed a number of mobile applications using our interactive system and conducted a user study where all participants preferred our unsupervised approach to supervised calibration. The demonstration of ViWatch is shown at https://youtu.be/N5-ggvy2qfI. 

As augmented and virtual reality (AR/VR) technology matures, a method is desired to represent real-world persons visually and aurally in a virtual scene with high fidelity to craft an immersive and realistic user experience. Current technologies leverage camera and depth sensors to render visual representations of subjects through avatars, and microphone arrays are employed to localize and separate high-quality subject audio through beamforming. However, challenges remain in both realms. In the visual domain, avatars can only map key features (e.g., pose, expression) to a predetermined model, rendering them incapable of capturing the subjects’ full details. Alternatively, high-resolution point clouds can be utilized to represent human subjects. However, such three-dimensional data is computationally expensive to process. In the realm of audio, sound source separation requires prior knowledge of the subjects’ locations. However, it may take unacceptably long for sound source localization algorithms to provide this knowledge, which can still be error-prone, especially with moving objects. These challenges make it difficult for AR systems to produce real-time, high-fidelity representations of human subjects for applications such as AR/VR conferencing that mandate negligible system latency. We present Acuity, a real-time system capable of creating high-fidelity representations of human subjects in a virtual scene both visually and aurally. Acuity isolates subjects from high-resolution input point clouds. It reduces the processing overhead by performing background subtraction at a coarse resolution, then applying the detected bounding boxes to fine-grained point clouds. Meanwhile, Acuity leverages an audiovisual sensor fusion approach to expedite sound source separation. The estimated object location in the visual domain guides the acoustic pipeline to isolate the subjects’ voices without running sound source localization. Our results demonstrate that Acuity can isolate multiple subjects’ high-quality point clouds with a maximum latency of 70 ms and average throughput of over 25 fps, while separating audio in less than 30 ms. We provide the source code of Acuity at: https://github.com/nesl/Acuity. 

Abstract There is tremendous interest in employing collective excitations of the lattice, spin, charge, and orbitals to tune strongly correlated electronic phenomena. We report such an effect in a ruthenate, Ca₃Ru₂O₇, where two phonons with strong electron-phonon coupling modulate the electronic pseudogap as well as mediate charge and spin density wave fluctuations. Combining temperature-dependent Raman spectroscopy with density functional theory reveals two phonons,B₂^PandB₂^M, that are strongly coupled to electrons and whose scattering intensities respectively dominate in the pseudogap versus the metallic phases. TheB₂^Psqueezes the octahedra along the out of planec-axis, while theB₂^Melongates it, thus modulating the Ru 4d orbital splitting and the bandwidth of the in-plane electron hopping; Thus,B₂^Popens the pseudogap, whileB₂^Mcloses it. Moreover, theB₂phonons mediate incoherent charge and spin density wave fluctuations, as evidenced by changes in the background electronic Raman scattering that exhibit unique symmetry signatures. The polar order breaks inversion symmetry, enabling infrared activity of these phonons, paving the way for coherent light-driven control of electronic transport.