This paper focuses on the task of speech-driven 3D facial animation, which aims to generate realistic and synchronized facial motions driven by speech inputs. Recent methods have employed audio-conditioned diffusion models for 3D facial animation, achieving impressive results in generating expressive and natural animations. However, these methods process the whole audio sequences in a single pass, which poses two major challenges: they tend to perform poorly when handling audio sequences that exceed the training horizon and will suffer from significant latency when processing long audio inputs. To address these limitations, we propose a novel autoregressive diffusion model that outputs facial motions in a streaming manner. This design ensures flexibility with varying audio lengths and achieves low latency independent of audio duration. Specifically, we select a limited number of past frames as historical motion context and combine them with the audio input to create a dynamic condition. This condition guides a lightweight diffusion head to iteratively generate facial motion frames, enabling real-time synthesis with high-quality results. Experiments conducted on public datasets demonstrate that our approach outperforms recent baseline methods.

This paper addresses the problem of decomposed 4D scene reconstruction from multi-view videos. Recent methods achieve this by lifting video segmentation results to a 4D representation through differentiable rendering techniques. Therefore, they heavily rely on the quality of video segmentation maps, which are often unstable, leading to unreliable reconstruction results. To overcome this challenge, our key idea is to represent the decomposed 4D scene with the Freetime FeatureGS and design a streaming feature learning strategy to accurately recover it from per-image segmentation maps, eliminating the need for video segmentation. Freetime FeatureGS models the dynamic scene as a set of Gaussian primitives with learnable features and linear motion ability, allowing them to move to neighboring regions over time. We apply a contrastive loss to Freetime FeatureGS, forcing primitive features to be close or far apart based on whether their projections belong to the same instance in the 2D segmentation map. As our Gaussian primitives can move across time, it naturally extends the feature learning to the temporal dimension, achieving 4D segmentation. Furthermore, we sample observations for training in a temporally ordered manner, enabling the streaming propagation of features over time and effectively avoiding local minima during the optimization process. Experimental results on several datasets show that the reconstruction quality of our method outperforms recent methods by a large margin.

While logging has been adopted in persistent memory (PM) to support crash consistency, logging incurs severe performance overhead. This paper discovers two common factors that contribute to the inefficiency of logging: (1) load imbalance among memory banks, and (2) constraints of intra-record ordering. Over-loaded memory banks may significantly prolong the waiting time of log requests targeting these banks. To address this issue, we propose a novel log entry allocation scheme (LALEA) that reshapes the traffic distribution over PM banks. In addition, the intra-record ordering between a header and its log entries decreases the degree of parallelism in log operations. We design a log metadata buffering scheme (BLOM) that eliminates the intra-record ordering constraints. These two proposed log optimizations are general and can be applied to many existing designs. We evaluate our designs using both micro-benchmarks and real PM applications. Our experimental results show that LALEA and BLOM can achieve 54.04% and 17.16% higher transaction throughput on average, compared to two state-of-the-art designs, respectively.

Shadow paging can guarantee crash consistency for Persistent Memory (PM). However, shadow paging requires the use of an address mapping table to track shadow pages, and frequent accesses to this table introduce significant performance overhead. In addition, maintaining crash consistency at the granularity level of a page causes a large amount of unnecessary write traffic. This paper proposes a novel hardware-assisted fine-grained out-place-update scheme at the granularity level of a cacheline to efficiently support crash consistency for PM. Our design fully leverages the Address Indirection Table (AIT) available in commodity PM to implement remapping. To ensure the atomicity and durability of AIT updates, we propose two policies: eager persisting and lazy persisting. We also employ overflow log to handle the eviction of speculative AIT cache entries upon an overflow in the AIT cache. Evaluation results based on multicore workloads demonstrate that our proposed scheme can improve the transaction throughput over the state-of-the-art design by 24.0% on average.

Logging is widely adopted to ensure crash consistency for Non-Volatile Memory (NVM) systems. However, the logging imposes significant performance overhead caused by the extra log operations and ordering constraints between the logging and in-place updates, degrading the system performance. There are some research efforts to reduce the logging overhead. Recently, LAD proposed that exploiting the non-volatility of Asynchronous DRAM Refresh (ADR) buffer can remove log operations for a transaction whose total amount of updated cachelines is smaller than the buffer capacity, ensuring crash consistency. However, on multi-core systems, concurrent transactions contend the scarce ADR buffer and frequently lead to the buffer overflow. Upon the buffer overflow, LAD resorts to logging operations for in-flight transactions, degrading the system performance. Our experiments show that LAD produces a significant number of log operations when multiple transactions run concurrently. To decrease log operations caused by LAD, this paper presents a new transaction execution scheme, called two-stage transaction execution(TSTE), which allows the write requests of a transaction to be in both the ADR buffer and the staging SRAM buffer. Our new scheme performs log operations for a transaction’s write requests in the SRAM buffer and executes in-place update operations for this transaction’s write requests in the ADR buffer. The introduced SRAM buffer can make the ADR buffer serve more update requests, reducing log operations.The evaluation results demonstrate that our proposed schemes can efficiently reduce log operations up to 39.29% and improve the transaction throughput up to 28.22%

Non- Volatile Memory (NVM) systems require logging to support crash consistency. However, log operations introduce severe performance overhead. Recently, LAD was proposed to eliminate log operations for some transactions in which the total amount of updated cachelines is smaller than the Asynchronous DRAM Refresh (ADR) buffer, without affecting crash consistency. Nevertheless, on multicore, concurrent transactions tend to exhaust the ADR resource and hence log operations have to be conducted in LAD. In this study, we observe that a significant number of log operations could be avoided if each transaction run alone. To eliminate these unnecessary log operations, this paper proposes virtual ADR buffers to decouple buffering from the ADR's reliable writing data. Specifically, only logless operations are allowed to access ADR resources. Additionally, this paper proposes to adopt redo log with DRAM cache to speed up the transaction commit speed. The evaluation results demonstrate that our proposed scheme can efficiently reduce log operation up to 94.9 % and improve the transaction throughput up to 78.6 %.