Integrated Magneto-photonic Non-Volatile Multi-Bit Memory

We present an integrated magneto-photonic device for all-optical switching of non-volatile multi-bit spintronic memory. The bits are based on stand-alone magneto-tunnel junctions which are perpendicularly magnetized with all-optically switchable free layers, coupled onto photonic crystal nanobeam cavities on an indium phosphide based platform. This device enables switching of the magnetization state of the bits by locally increasing the power absorption of light at resonance with the cavity. We design an add/drop network of cavities to grant random access to multiple bits via a wavelength-division multiplexing scheme. Based on a three-dimensional finite-difference time-domain method, we numerically illustrate a compact device capable of switching and accessing 8 bits in different cavities with a 5 nm wavelength spacing in the conventional (C) telecommunication band. Our multi-bit device holds promise as a new paradigm for developing an ultrafast photonically-addressable spintronic memory and may also empower novel opportunities for photonically-driven spintronic-based neuromorphic computing.Comment: 21 pages, 6 figures, 1 tabl

Radar Sensing via OTFS Signaling: A Delay Doppler Signal Processing Perspective

The recently proposed orthogonal time frequency space (OTFS) modulation multiplexes data symbols in the delay-Doppler (DD) domain. Since the range and velocity, which can be derived from the delay and Doppler shifts, are the parameters of interest for radar sensing, it is natural to consider implementing DD signal processing for radar sensing. In this paper, we investigate the potential connections between the OTFS and DD domain radar signal processing. Our analysis shows that the range-Doppler matrix computing process in radar sensing is exactly the demodulation of OTFS with a rectangular pulse shaping filter. Furthermore, we propose a two-dimensional (2D) correlation-based algorithm to estimate the fractional delay and Doppler parameters for radar sensing. Simulation results show that the proposed algorithm can efficiently obtain the delay and Doppler shifts associated with multiple targets.Comment: ICC-2023 Accepte

Privacy-preserving Fine-tuning of Large Language Models through Flatness

The privacy concerns associated with the use of Large Language Models (LLMs) have grown recently with the development of LLMs such as ChatGPT. Differential Privacy (DP) techniques are explored in existing work to mitigate their privacy risks at the cost of generalization degradation. Our paper reveals that the flatness of DP-trained models' loss landscape plays an essential role in the trade-off between their privacy and generalization. We further propose a holistic framework to enforce appropriate weight flatness, which substantially improves model generalization with competitive privacy preservation. It innovates from three coarse-to-grained levels, including perturbation-aware min-max optimization on model weights within a layer, flatness-guided sparse prefix-tuning on weights across layers, and weight knowledge distillation between DP \& non-DP weights copies. Comprehensive experiments of both black-box and white-box scenarios are conducted to demonstrate the effectiveness of our proposal in enhancing generalization and maintaining DP characteristics. For instance, on text classification dataset QNLI, DP-Flat achieves similar performance with non-private full fine-tuning but with DP guarantee under privacy budget $\epsilon=3$, and even better performance given higher privacy budgets. Codes are provided in the supplement.Comment: Accepted to ICLR 2024 SeT LLM Worksho

From OTFS to DD-ISAC: Integrating Sensing and Communications in the Delay Doppler Domain

Next-generation vehicular networks are expected to provide the capability of robust environmental sensing in addition to reliable communications to meet intelligence requirements. A promising solution is the integrated sensing and communication (ISAC) technology, which performs both functionalities using the same spectrum and hardware resources. Most existing works on ISAC consider the Orthogonal Frequency Division Multiplexing (OFDM) waveform. Nevertheless, vehicle motion introduces Doppler shift, which breaks the subcarrier orthogonality and leads to performance degradation. The recently proposed Orthogonal Time Frequency Space (OTFS) modulation, which exploits various advantages of Delay Doppler (DD) channels, has been shown to support reliable communication in high-mobility scenarios. Moreover, the DD waveform can directly interact with radar sensing parameters, which are actually delay and Doppler shifts. This paper investigates the advantages of applying the DD communication waveform to ISAC. Specifically, we first provide a comprehensive overview of implementing DD communications, based on which several advantages of DD-ISAC over OFDM-based ISAC are revealed, including transceiver designs and the ambiguity function. Furthermore, a detailed performance comparison are presented, where the target detection probability and the mean squared error (MSE) performance are also studied. Finally, some challenges and opportunities of DD-ISAC are also provided.Comment: Magazine paper submitted to IEE

Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy

Sparsely activated Mixture-of-Experts (SMoE) has shown promise to scale up the learning capacity of neural networks, however, they have issues like (a) High Memory Usage, due to duplication of the network layers into multiple copies as experts; and (b) Redundancy in Experts, as common learning-based routing policies suffer from representational collapse. Therefore, vanilla SMoE models are memory inefficient and non-scalable, especially for resource-constrained downstream scenarios. In this paper, we ask: Can we craft a compact SMoE model by consolidating expert information? What is the best recipe to merge multiple experts into fewer but more knowledgeable experts? Our pilot investigation reveals that conventional model merging methods fail to be effective in such expert merging for SMoE. The potential reasons are: (1) redundant information overshadows critical experts; (2) appropriate neuron permutation for each expert is missing to bring all of them in alignment. To address this, we propose M-SMoE, which leverages routing statistics to guide expert merging. Specifically, it starts with neuron permutation alignment for experts; then, dominant experts and their "group members" are formed; lastly, every expert group is merged into a single expert by utilizing each expert's activation frequency as their weight for merging, thus diminishing the impact of insignificant experts. Moreover, we observed that our proposed merging promotes a low dimensionality in the merged expert's weight space, naturally paving the way for additional compression. Hence, our final method, MC-SMoE (i.e., Merge, then Compress SMoE), further decomposes the merged experts into low-rank and structural sparse alternatives. Extensive experiments across 8 benchmarks validate the effectiveness of MC-SMoE. For instance, our MC-SMoE achieves up to 80% memory and a 20% FLOPs reduction, with virtually no loss in performance.Comment: This paper is accepted in ICLR 202

Optical Reading of Nanoscale Magnetic Bits in an Integrated Photonic Platform

In this paper, we propose a compact integrated hybrid plasmonic-photonic device for optical reading of nanoscale magnetic bits with perpendicular magnetic anisotropy in a magnetic racetrack on top of a photonic waveguide on the indium phosphide membrane on silicon platform. The hybrid device is constructed by coupling a doublet of V-shaped gold plasmonic nanoantennas on top of the indium phosphide waveguide. By taking advantage of the localized surface plasmons, our hybrid device can enable detection of the magnetization state in magnetic bits beyond the diffraction limit of light and enhance the polar magneto-optical Kerr effect (PMOKE). We further illustrate how combining the hybrid device with a plasmonic polarization rotator provides magneto-optical read-out by transforming the PMOKE-induced polarization change into an intensity variation of the waveguide mode. According to the simulation results based on a three-dimensional finite-difference time-domain method, the hybrid device can detect the magnetization states in targeted bits in a magnetic racetrack medium down to ~ 100x100 nm2, regardless of the magnetization state of the rest of the racetrack with a relative intensity contrast of greater than 0.5% for a ~ 200x100 nm2 magnetic bit. We believe our hybrid device can be an enabling technology that can connect integrated photonics with nanoscale spintronics, paving the way toward ultrafast and energy efficient advanced on-chip applications

Grid Evolution for Doubly Fractional Channel Estimation in OTFS Systems

In orthogonal time-frequency space communications, the performances of existing on-grid and off-grid channel estimation (CE) schemes are determined by the delay-Doppler (DD) grid density. In practice, multiple real-life DD channel responses might be co-located within a same DD grid interval, leading to performance degradation. A finer grid interval is needed to distinguish these responses, but this could result in a significantly higher CE complexity when traditional methods are used. To address this issue, a grid evolution method for doubly fractional CE is proposed by evolving the initially uniform coarse DD grid into a non-uniform dense grid. Simulation results show that our proposed method leads to improved computational efficiency, and achieves a good trade-off between CE performance and complexity

Design of an integrated hybrid plasmonic-photonic device for all-optical switching and reading of spintronic memory

We introduce a novel integrated hybrid plasmonic-photonic device for all-optical switching and reading of nanoscale ferrimagnet bits. The racetrack memory made of synthetic ferrimagnetic material with a perpendicular magnetic anisotropy is coupled on to a photonic waveguide onto the indium phosphide membrane on silicon platform. The device which is composed of a double V-shaped gold plasmonic nanoantenna coupled with a photonic crystal cavity can enable switching and reading of the magnetization state in nanoscale magnetic bits by enhancing the absorbed energy density and polar magneto-optical Kerr effect (PMOKE) locally beyond the diffraction limit. Using a three-dimensional finite-difference time-domain method, we numerically show that our device can switch and read the magnetization state in targeted bits down to ~100 nm in the presence of oppositely magnetized background regions in the racetrack with widths of 30 to 120 nm, clearly outperforming a bare photonic waveguide. Our hybrid device tackles the challenges of nonlinear absorption in the waveguide, weak PMOKE, and size mismatch between spintronics and integrated photonics. Thus, it provides missing link between the integrated photonics and nanoscale spintronics, expediting the development of ultrafast and energy efficient advanced on-chip applications

Viruses mobilize plant immunity to deter nonvector insect herbivores.

A parasite-infected host may promote performance of associated insect vectors; but possible parasite effects on nonvector insects have been largely unexplored. Here, we show that Begomovirus, the largest genus of plant viruses and transmitted exclusively by whitefly, reprogram plant immunity to promote the fitness of the vector and suppress performance of nonvector insects (i.e., cotton bollworm and aphid). Infected plants accumulated begomoviral βC1 proteins in the phloem where they were bound to the plant transcription factor WRKY20. This viral hijacking of WRKY20 spatiotemporally redeployed plant chemical immunity within the leaf and had the asymmetrical benefiting effects on the begomoviruses and its whitefly vectors while negatively affecting two nonvector competitors. This type of interaction between a parasite and two types of herbivores, i.e., vectors and nonvectors, occurs widely in various natural and agricultural ecosystems; thus, our results have broad implications for the ecological significance of parasite-vector-host tripartite interactions