Disaggregated storage architectures have become a critical component in modern data centers, offering independent scaling of compute and storage. However, disaggregation introduces performance challenges, particularly due to the overhead of remote storage access. We first conduct a detailed end-to-end analysis of existing caching strategies and identify the critical issues, such as high server resource consumption, data duplication, lack of fair server resources scheduling, inefficient eviction, and prefetching policies. To address these limitations, we present the preliminary design of OrcaCache, an orchestrated, unified caching framework that coordinates between clients and storage servers. OrcaCache aims to carefully shift cache indexing to clients by exposing a global cache view, with an aim to reduce server CPU usage and duplication of data across caches. OrcaCache also aims to improve cache efficiency, adaptiveness, and fairness across servers and clients.

Neural Volume Rendering (NVR), a novel paradigm for the longstanding problem of photo-realistic rendering of virtual worlds, has developed explosively in the past three years. The unique and substantial computational requirements of NVR pose challenge on deploying NVR to existing dedicated accelerator for neural networks. In this work, we propose ZeroTetris, a spacial feature similarity-based sparse multilayer perceptron (MLP) hardware accelerator for NVR. By leveraging the unique similarity-based sparsity between adjacent sampling points in NVR models, ZeroTetris efficiently bypass the computation of zero activations, thereby enhancing energy efficiency. Evaluation results affirm the effectiveness of the proposed design, showcasing ZeroTetris's superior performance in both area and power efficiency compared to other dedicated sparse matrix multiplication or MLP accelerator designs.