Recently, Video-Language Models (VideoLMs) have demonstrated remarkable capabilities, offering significant potential for flexible and powerful video query systems. These models typically rely on Vision Transformers (ViTs), which process video frames individually to extract visual embeddings. However, generating embeddings for large-scale videos requires ViT inferencing across numerous frames, posing a major hurdle to real-world deployment and necessitating solutions for integration into scalable video data management systems. This paper introduces Déjà Vu, a video-language query engine that accelerates ViT-based VideoLMs by reusing computations across consecutive frames. At its core is ReuseViT, a modified ViT model specifically designed for VideoLM tasks, which learns to detect inter-frame reuse opportunities, striking an effective balance between accuracy and reuse. Although ReuseViT significantly reduces computation, these savings do not directly translate into performance gains on GPUs. To overcome this, Déjà Vu integrates memory-compute joint compaction techniques that convert the FLOP savings into tangible performance gains. Evaluations on three VideoLM tasks show that Déjà Vu accelerates embedding generation by up to a 2.64× within a 2% error bound, dramatically enhancing the practicality of VideoLMs for large-scale video analytics.

Toddlers evolve from free exploration with sparse feedback to exploiting prior experiences for goal-directed learning with denser rewards. Drawing inspiration from this Toddler-Inspired Reward Transition, we set out to explore the implications of varying reward transitions when incorporated into Reinforcement Learning (RL) tasks. Central to our inquiry is the transition from sparse to potential-based dense rewards, which share optimal strategies regardless of reward changes. Through various experiments, including those in egocentric navigation and robotic arm manipulation tasks, we found that proper reward transitions significantly influence sample efficiency and success rates. Of particular note is the efficacy of the toddler-inspired Sparse-to-Dense (S2D) transition. Beyond these performance metrics, using Cross-Density Visualizer technique, we observed that transitions, especially the S2D, smooth the policy loss landscape, promoting wide minima that enhance generalization in RL models.

With the ever increasing prevalence of neural networks and the upheaval from the language models, it is time to rethink neural acceleration. Up to this point, the broader research community, including ourselves, has disproportionately focused on GEneral Matrix Multiplication (GEMM) operations. The supporting argument was that the large majority of the neural operations are GEMM. This argument guided the research in Neural Processing Units (NPUs) for the last decade. However, scant attention was paid to non-GEMM operations and they are rather overlooked. As deep learning evolved and progressed, these operations have grown in diversity and also large variety of structural patterns have emerged that interweave them with the GEMM operations. However, conventional NPU designs have taken rather simplistic approaches by supporting these operations through either a number of dedicated blocks or fall back to general-purpose processors.

Deep neural network (DNN) video analytics is crucial for autonomous systems such as self-driving vehicles, unmanned aerial vehicles (UAVs), and security robots. However, real-world deployment faces challenges due to their limited computational resources and battery power. To tackle these challenges, continuous learning exploits a lightweight “student” model at deployment (inference), leverages a larger “teacher” model for labeling sampled data (labeling), and continuously retrains the student model to adapt to changing scenarios (retraining). This paper highlights the limitations in state-of-theart continuous learning systems: (1) they focus on computations for retraining, while overlooking the compute needs for inference and labeling, (2) they rely on power-hungry GPUs, unsuitable for battery-operated autonomous systems, and (3) they are located on a remote centralized server, intended for multi-tenant scenarios, again unsuitable for autonomous systems due to privacy, network availability, and latency concerns. We propose a hardwarealgorithm co-designed solution for continuous learning, DACAPO, that enables autonomous systems to perform concurrent executions of inference, labeling, and retraining in a performant and energy-efficient manner. DACapo comprises (1) a spatiallypartitionable and precision-flexible accelerator enabling parallel execution of kernels on sub-accelerators at their respective precisions, and (2) a spatiotemporal resource allocation algorithm that strategically navigates the resource-accuracy tradeoff space, facilitating optimal decisions for resource allocation to achieve maximal accuracy. Our evaluation shows that DACAPO achieves $\mathbf{6. 5 \%}$ and $\mathbf{5. 5 \%}$ higher accuracy than a state-of-theart GPU-based continuous learning systems, Ekya and EOMU, respectively, while consuming $254 \times$ less power.

In this paper, we propose a new beamforming design method to address the effect of beam squint in Terahertz communication. Conventional schemes for mitigating the beam squint have encountered challenges, such as high power consumption due to additional complex hardware or impractical computational complexity. To overcome the limitations, we propose a new analog beamforming technique, which does not require not only a high computational complexity but also additional hardware. Specifically, our proposed scheme performs analog beamforming at symmetrically shifted frequencies using a variable ϵ to create a robust wide beamformer in frequency domain, thereby minimizing the distortion of the array gain throughout the entire bandwidth. Furthermore, the value of ϵ is expressed in a closed form based on the lower bound of achievable spectral efficiency. Our simulation results show that the proposed scheme outperforms conventional hybrid beamformer and other techniques to deal with the beam squint.

Learning in a multi-target environment without prior knowledge about the targets requires a large amount of samples and makes generalization difficult. To solve this problem, it is important to be able to discriminate targets through semantic understanding. In this paper, we propose goal-aware cross-entropy (GACE) loss, that can be utilized in a self-supervised way using auto-labeled goal states alongside reinforcement learning. Based on the loss, we then devise goal-discriminative attention networks (GDAN) which utilize the goal-relevant information to focus on the given instruction. We evaluate the proposed methods on visual navigation and robot arm manipulation tasks with multi-target environments and show that GDAN outperforms the state-of-the-art methods in terms of task success ratio, sample efficiency, and generalization. Additionally, qualitative analyses demonstrate that our proposed method can help the agent become aware of and focus on the given instruction clearly, promoting goal-directed behavior.

We develop hierarchically quantized efficient embedding representations for similarity-based search and show that this representation provides not only the state of the art performance on the search accuracy but also provides several orders of speed up during inference. The idea is to hierarchically quantize the representation so that the quantization granularity is greatly increased while maintaining the accuracy and keeping the computational complexity low. We also show that the problem of finding the optimal sparse compound hash code respecting the hierarchical structure can be optimized in polynomial time via minimum cost flow in an equivalent flow network. This allows us to train the method end-to-end in a mini-batch stochastic gradient descent setting. Our experiments on Cifar100 and ImageNet datasets show the state of the art search accuracy while providing several orders of magnitude search speedup respectively over exhaustive linear search over the dataset.