Diffusion-based text-to-speech (TTS) systems have made remarkable progress in zero-shot speech synthesis, yet optimizing all components for perceptual metrics remains challenging. Prior work with DMOSpeech demonstrated direct metric optimization for speech generation components, but duration prediction remained unoptimized. This paper presents DMOSpeech 2, which extends metric optimization to the duration predictor through a reinforcement learning approach. The proposed system implements a novel duration policy framework using group relative preference optimization (GRPO) with speaker similarity and word error rate as reward signals. By optimizing this previously unoptimized component, DMOSpeech 2 creates a more complete metric-optimized synthesis pipeline. Additionally, this paper introduces teacher-guided sampling, a hybrid approach leveraging a teacher model for initial denoising steps before transitioning to the student model, significantly improving output diversity while maintaining efficiency. Comprehensive evaluations demonstrate superior performance across all metrics compared to previous systems, while reducing sampling steps by half without quality degradation. These advances represent a significant step toward speech synthesis systems with metric optimization across multiple components.

Optimizing energy consumption for computing workloads is a critical challenge, especially as we push the limits of high-performance systems within resource constraints. An underexplored approach to reducing energy costs is exploiting value locality: the tendency of instructions to process similar operand values during execution. Instruction operand values have abundant similarities within the reachable instruction window, a factor that existing architectures currently ignore. This paper introduces the broad concept of instruction-level, value-aware scheduling, an approach that leverages value locality in the hardware scheduler. By redirecting instructions of similar operand values to move together down the pipeline, the scheduler can reduce switching activity and dynamic energy usage. Such valueaware design philosophy, capable of adapting to a diverse range of workloads and architectures, has potential to be applied in broader architectural improvements.

Function-as-a-Service (FaaS) continues to be a popular and growing way to access abundant cloud resources. Both the research community and industry have begun investigating designing new or modifying existing processors for FaaS. While there exists many traditional computer architecture simulators, the complex, full-system and full-stack nature of FaaS workloads has opened the door to needing more purpose-built FaaS-specific simulators along with easy to use, simulation-friendly, characteristic workloads. In order to fully study FaaS workloads, the simulator needs to run a full OS running many concurrent containers with a full-stack serverless platform like OpenWhisk. This paper characterizes and describes MindPalace which is a simulator and framework designed to investigate microarchitecture innovations for FaaS workloads. MindPalace utilizes both QEMU and ChampSim. With QEMU, MindPalace is able to run complex, full-system and full-stack workloads including running the Apache OpenWhisk platform. By leveraging ChampSim, MindPalace can conduct detailed computer architecture and micro-architecture research and has access to advanced micro-architecture models for branch predictors and prefetchers. In addition, MindPalace uses a VM with Apache OpenWhisk running. In this work, we compare MindPalace to other simulators and use MindPalace to investigate FaaS-specific architecture ideas including a design which reloads branch predictor state between function invocations. We also characterize MindPalace and study the accuracy of simulation versus real hardware in terms of branch prediction rates, cache miss rates, and IPC.

The increasing popularity of Function-as-a-Service (FaaS) applications has created new challenges for current microarchitecture designs due to their complex middleware and significant Operating System (OS) overhead, prohibiting the detailed study of new hardware designs. This work introduces MindPalace, an open-source full-system framework that developed to study the architectural behavior of large middleware serverless systems. MindPalace contains a simulation infrastructure and a pre-configured virtual machine (VM) to assist FaaS architecture research. The simulation tool leverages QEMU and ChampSim, combining a featureful full-system emulator with a rich microarchitecture simulator. The VM contains a full Ubuntu image with preinstalled Open Whisk, together with checkpoints for ready-to-run serverless applications. Combining them enables full-system FaaS architectural study of the memory hierarchy, branch predictors, prefetchers, and other microarchitectural components.

The Web as a global information space is developing from a Web of documents to a Web of data. This development opens new ways for addressing complex information needs. Search is no longer limited to matching keywords against documents, but instead complex information needs can be expressed in a structured way, with precise answers as results. In this paper, we demonstrate Hermes, an infrastructure for data web search. To provide an end-user oriented interface, we support expressive keyword search by translating user information needs into structured queries. We integrate heterogeneous web data sources with automatically computed mappings. Schema-level mappings are exploited in constructing structured queries against the integrated schema. These structured queries are decomposed into queries against the local web data sources, which are then processed in a distributed way.