Neural Radiance Field (NeRF) based 3D reconstruction is highly desirable for immersive Augmented and Virtual Reality (AR/VR) applications, but achieving instant (i.e., < 5 seconds) on-device NeRF training remains a challenge. In this work, we first identify the inefficiency bottleneck: the need to interpolate NeRF embeddings up to 200,000 times from a 3D embedding grid during each training iteration. To alleviate this, we propose Instant-3D, an algorithm-hardware co-design acceleration framework that achieves instant on-device NeRF training. Our algorithm decomposes the embedding grid representation in terms of color and density, enabling computational redundancy to be squeezed out by adopting different (1) grid sizes and (2) update frequencies for the color and density branches. Our hardware accelerator further reduces the dominant memory accesses for embedding grid interpolation by (1) mapping multiple nearby points' memory read requests into one during the feed-forward process, (2) merging embedding grid updates from the same sliding time window during back-propagation, and (3) fusing different computation cores to support the different grid sizes needed by the color and density branches of Instant-3D algorithm. Extensive experiments validate the effectiveness of Instant-3D, achieving a large training time reduction of 41× - 248× while maintaining the same reconstruction quality. Excitingly, Instant-3D has enabled instant 3D reconstruction for AR/VR, requiring a reconstruction time of only 1.6 seconds per scene and meeting the AR/VR power consumption constraint of 1.9 W.

The remarkable advancements in artificial intelligence (AI), primarily driven by deep neural networks, are facing challenges surrounding unsustainable computational trajectories, limited robustness, and a lack of explainability. To develop next-generation cognitive AI systems, neuro-symbolic AI emerges as a promising paradigm, fusing neural and symbolic approaches to enhance interpretability, robustness, and trustworthiness, while facilitating learning from much less data. Recent neuro-symbolic systems have demonstrated great potential in collaborative human-AI scenarios with reasoning and cognitive capabilities. In this paper, we aim to understand the workload characteristics and potential architectures for neuro-symbolic AI. We first systematically categorize neuro-symbolic AI algorithms, and then experimentally evaluate and analyze them in terms of runtime, memory, computational operators, sparsity, and system characteristics on CPUs, GPUs, and edge SoCs. Our studies reveal that neuro-symbolic models suffer from inefficiencies on off-the-shelf hardware, due to the memory-bound nature of vector-symbolic and logical operations, complex flow control, data dependencies, sparsity variations, and limited scalability. Based on profiling insights, we suggest cross-layer optimization solutions and present a hardware acceleration case study for vector-symbolic architecture to improve the performance, efficiency, and scalability of neuro-symbolic computing. Finally, we discuss the challenges and potential future directions of neuro-symbolic AI from both system and architectural perspectives.

Gamboge is the dry resin secreted by Garcinia hanburyi Hook.f, with the function of promoting blood circulation, detoxification, hemostasis and killing insects, used for the treatment of cancer, brain edema and other diseases. Gambogic acid is the main effective constituent of Gamboge. The present study investigated the protective effects of gambogic acid on spinal cord injury (SCI) and its anti‑inflammatory mechanism in an SCI model in vivo. Basso, Beattie and Bresnahan (BBB) testing was used to detect the protective effects of gambogic acid on nerve function of SCI rats. The water content of the spinal cord was used to analyze the protective effects of gambogic acid on the damage of SCI. Treatment with gambogic acid effectively improved BBB scores and inhibited water content of the spinal cord in SCI rats. Also, gambogic acid significantly reduced inflammatory cytokines levels of [tumor necrosis factor‑α, interleukin (IL)‑6, IL‑12 and IL‑1β] and oxidative stress (malondialdehyde, superoxide dismutase, glutathione and glutathione‑peroxidase) factors, and suppressed receptor activator of nuclear factor κB ligand, phosphorylated p38 protein expression and toll‑like receptor 4/nuclear factor‑κB pathway activation, and increased phosphatidylinositol 3‑kinase/protein kinase B (Akt) pathway activation in SCI rats. These results provide evidence that gambogic acid inhibits SCI and inflammation through suppressing the p38 and Akt signaling pathways.

Eye tracking has become an essential human-machine interaction modality in virtual and augmented reality (VR/AR) applications requiring high throughput (e.g., >240 FPS), small form-factor, and enhanced visual privacy. Existing eye tracking systems have adopted bulky lens-based cameras, and thus suffer from both a large form-factor and high communication cost between the camera and backend processor. This work presents a camera, algorithm, and accelerator co-designed lensless eye tracking system dubbed EyeCoD, which to the best of our knowledge is the first to provide a general front-end eye tracking solution for AR/VR while satisfying the requirements for both high throughput and smaller form-factor. Specifically, EyeCoD integrates system-, algorithm-, and accelerator-level techniques to boost system efficiency without sacrificing the eye tracking accuracy. We believe that our EyeCoD system will pave the way for next-generation eye tracking solutions in VR/AR and can shed light on future innovations for intelligent imaging systems.