Novel View Synthesis is a foundational technique for creating immersive Augmented and Virtual Reality (AR/VR) experiences, aiming to generate photorealistic images of a scene from arbitrary camera viewpoints using only a limited set of source images, with Neural Radiance Fields (NeRF) emerging as the state-of-the-art solution. However, real-time NeRF rendering on low-power devices remains challenging due to its memory-intensive hash encoding and compute-intensive Multilayer Perception (MLP). In this work, we propose Instant-CIM, the fully on-chip Computing-in-Memory (CIM) architecture for efficient NeRF rendering. At the algorithm level, Instant-CIM proposes a spatially-adaptive framework that dynamically selects the number of active hash encoding levels per spatial region based on a composite importance score derived from density and gradient. The approach replaces uniform level allocation with a threshold-based strategy that activates finer encoding levels only in regions with high representation complexity. At the hardware level, Instant-CIM proposes an in-situ hash engine that implements in-memory hash query and interpolation through 3D scene grid decomposition and Z-order based mapping schemes. Meanwhile, Instant-CIM proposes a sparse MLP engine that leverages differential-based input complemented by a precision-adjustable skipping mechanism to fully exploit spatial similarities. Comprehensive evaluation across synthetic datasets demonstrates that Instant-CIM achieves 3.0×~4.9× improvement in rendering speed and 8.6×~33× enhancement in energy efficiency compared to state-of-the-art NeRF architecture.

The increasing adoption of large-scale models under 7 billion parameters in both language and vision domains enables inference tasks on a single consumer-grade GPU but makes fine-tuning models of this scale, especially 7B models, challenging. This limits the applicability of pruning methods that require full fine-tuning. Meanwhile, pruning methods that do not require fine-tuning perform well at low sparsity levels (10%-50%) but struggle at mid-to-high sparsity levels (50%-70%), where the error behaves equivalently to that of semi-structured pruning. To address these issues, this paper introduces ICP, which finds a balance between full fine-tuning and zero fine-tuning. First, Sparsity Rearrange is used to reorganize the predefined sparsity levels, followed by Block-wise Compensate Pruning, which alternates pruning and compensation on the model’s backbone, fully utilizing inference results while avoiding full model fine-tuning. Experiments show that ICP improves performance at mid-to-high sparsity levels compared to baselines, with only a slight increase in pruning time and no additional peak memory overhead.

Sparse-view computed tomography (CT) reduces radiation exposure by acquiring fewer projections, making it a valuable tool in clinical scenarios where low-dose radiation is essential. However, this often results in increased noise and artifacts due to limited data. In this paper we propose a novel 3D Gaussian representation (3DGR) based method for sparse-view CT reconstruction. Inspired by recent success in novel view synthesis driven by 3D Gaussian splatting, we leverage the efficiency and expressiveness of 3D Gaussian representation as an alternative to implicit neural representation. To unleash the potential of 3DGR for CT imaging scenario, we propose two key innovations: (i) FBP-image-guided Guassian initialization and (ii) efficient integration with a differentiable CT projector. Extensive experiments and ablations on diverse datasets demonstrate the proposed 3DGR-CT consistently outperforms state-of-the-art counterpart methods, achieving higher reconstruction accuracy with faster convergence. Furthermore, we showcase the potential of 3DGR-CT for real-time physical simulation, which holds important clinical applications while challenging for implicit neural representations. Code available at: https://github.com/SigmaLDC/3DGR-CT.

Continuous motion prediction plays a significant role in realizing seamless control of robotic exoskeletons and orthoses. Explicitly modeling the relationship between coordinated muscle activations from surface electromyography (sEMG) and human limb movements provides a new path of sEMG-based human–machine interface. Instead of the numeric features from individual channels, we propose a muscle synergy-driven adaptive network-based fuzzy inference system (ANFIS) approach to predict continuous knee joint movements, in which muscle synergy reflects the motor control information to coordinate muscle activations for performing movements. Four human subjects participated in the experiment while walking at five types of speed: 2.0 km/h, 2.5 km/h, 3.0 km/h, 3.5 km/h, and 4.0 km/h. The study finds that the acquired muscle synergies associate the muscle activations with human joint movements in a low-dimensional space and have been further utilized for predicting knee joint angles. The proposed approach outperformed commonly used numeric features from individual sEMG channels with an average correlation coefficient of 0.92$ \pm $0.05. Results suggest that the correlation between muscle activations and knee joint movements is captured by the muscle synergy-driven ANFIS model and can be utilized for the estimation of continuous joint angles.