Diffusion-based text-to-image generation models trade latency for quality: small models are fast but generate lower quality images, while large models produce better images but are slow. We present MoDM, a novel caching-based serving system for diffusion models that dynamically balances latency and quality through a mixture of diffusion models. Unlike prior approaches that rely on model-specific internal features, MoDM caches final images, allowing seamless retrieval and reuse across multiple diffusion model families. This design enables adaptive serving by dynamically balancing latency and image quality: using smaller models for cache-hit requests to reduce latency while reserving larger models for cache-miss requests to maintain quality. Small model image quality is preserved using retrieved cached images. We design a global monitor that optimally allocates GPU resources and balances inference workload, ensuring high throughput while meeting Service-Level Objectives (SLOs) under varying request rates. Our evaluations show that MoDM significantly reduces average serving time by 2.5× while retaining image quality, making it a practical solution for scalable and resource-efficient model deployment.

Mixture of Experts (MoE) LLMs, with sparse activation patterns, offer a promising approach to scaling language models while avoiding proportional cost increases. However, their large parameter sizes pose deployment challenges in resource-constrained environments with limited GPU memory. Typical deployments use CPU-GPU hybrid execution, where the GPU handles compute-intensive GEMM operations and the CPU processes the attention mechanism. This setup introduces a challenge: optimizing resource utilization across CPU and GPU. Prior work designs system optimizations based on performance models with a limited scope that don’t capture complex hardware-system interactions. Therefore, they neither identify nor achieve hardware limits.

Oblivious RAM (ORAM) hides the memory access patterns, enhancing data privacy by preventing attackers from discovering sensitive information based on the sequence of memory accesses. The performance of ORAM is often limited by its inherent trade-off between security and efficiency, as concealing memory access patterns imposes significant computational and memory overhead. While prior works focus on improving the ORAM performance by prefetching and eliminating ORAM requests, we find that their performance is very sensitive to workload locality behavior and incurs additional management overhead caused by the ORAM stash pressure. This paper presents Palermo: a protocol-hardware co-design to improve ORAM performance. The key observation in Palermo is that classical ORAM protocols enforce restrictive dependencies between memory operations that result in low memory bandwidth utilization. Palermo introduces a new protocol that overlaps large portions of memory operations, within a single and between multiple ORAM requests, without breaking correctness and security guarantees. Subsequently, we propose an ORAM controller architecture that executes the proposed protocol to service ORAM requests. The hardware is responsible for concurrently issuing memory requests as well as imposing the necessary dependencies to ensure a consistent view of the ORAM tree across requests. Using a rich workload mix, we demonstrate that Palermo outperforms the RingORAM baseline by 2.9 ×, on average, incurring a negligible area overhead of 5.78mm2(less than 2% in 12th generation Intel CPU after technology scaling) and 2.14W without sacrificing security. We further show that Palermo also outperforms the state-of-the-art works PageORAM, PrORAM, and IR-ORAM.

Genome sequence analysis is fundamental to medical breakthroughs such as developing vaccines, enabling genome editing, and facilitating personalized medicine. The exponentially expanding sequencing datasets and complexity of sequencing algorithms necessitate performance enhancements. While the performance of software solutions is constrained by their underlying hardware platforms, the utility of fixed-function accelerators is restricted to only certain sequencing algorithms.This paper presents QUETZAL, the first general-purpose vector acceleration framework designed for high efficiency and broad applicability across a diverse set of genomics algorithms. While a commercial CPU’s vector datapath is a promising candidate to exploit the data-level parallelism in genomics algorithms, our analysis finds that its performance is often limited due to long-latency scatter/gather memory instructions. QUETZAL introduces a hardware-software co-design comprising an accelerator microarchitecture closely integrated with the CPU’s vector datapath, alongside novel vector instructions to fully capitalize on the proposed hardware. QUETZAL integrates a set of scratchpad-style buffers meticulously designed to minimize latency associated with scatter/gather instructions during the retrieval of input genome sequences data. QUETZAL supports both short and long reads, and different types of sequencing data formats. A combination of hardware and software techniques enables QUETZAL to reduce the latency of memory instructions, perform complex computation using a single instruction, and transform data representations at runtime, resulting in overall efficiency gain. QUETZAL significantly accelerates a vectorized CPU baseline on modern genome sequence analysis algorithms by 5.7×, while incurring a small area overhead of 1.4% post place-and-route at the 7nm technology node compared to an HPC ARM CPU.

Despite the high computational throughput of GPUs, limited memory capacity and bandwidth-limited CPU-GPU communication via PCIe links remain significant bottlenecks for accelerating large-scale data analytics workloads. This paper introduces Vortex, a GPU-accelerated framework designed for data analytics workloads that exceed GPU memory capacity. A key aspect of our framework is an optimized IO primitive that leverages all available PCIe links in multi-GPU systems for the IO demand of a single target GPU. It routes data through other GPUs to such target GPU that handles IO-intensive analytics tasks. This approach is advantageous when other GPUs are occupied with compute-bound workloads, such as popular AI applications that typically underutilize IO resources. We also introduce a novel programming model that separates GPU kernel development from IO scheduling, reducing programmer burden and enabling GPU code reuse. Additionally, we present the design of certain important query operators and discuss a late materialization technique based on GPU's zero-copy memory access. Without caching any data in GPU memory, Vortex improves the performance of the state-of-the-art GPU baseline, Proteus, by 5.7× on average and enhances price performance by 2.5× compared to a CPU-based DuckDB baseline.

Temporal motif mining is the task of finding the occurrences of subgraph patterns within a large input temporal graph that obey the specified structural and temporal constraints. Despite its utility in several critical application domains that demand high performance ( e.g. , detecting fraud in financial transaction graphs), the performance of existing software is limited on commercial hardware platforms, in that it runs for tens of hours. This paper presents Everest---a system that efficiently maps the workload of mining (supports both enumeration and counting) temporal motifs to the highly parallel GPU architecture. In particular, using an input temporal graph and a more expressive user-defined temporal motif query definition compared to prior works, Everest generates an execution plan and runtime primitives that optimize the workload execution by exploiting the high compute throughput of a GPU. Everest generates motif-specific mining code to reduce long-latency memory accesses and frequent thread divergence operations. Everest incorporates novel low-cost runtime mechanisms to enable load balancing to improve GPU hardware utilization. To support large graphs that do not fit on GPU memory, Everest also supports multi-GPU execution by intelligently partitioning the edge list that prevents inter-GPU communication. Everest hides the implementation complexity of presented optimizations away from the targeted system user for better usability. Our evaluation shows that, using proposed optimizations, Everest improves the performance of a baseline GPU implementation by 19X, on average.

A variety of complex systems, including social and communication networks, financial markets, biology, and neuroscience are modeled using temporal graphs that contain a set of nodes and directed timestamped edges. Temporal motifs in temporal graphs are generalized from subgraph patterns in static graphs in that they also account for edge ordering and time duration, in addition to the graph structure. Mining temporal motifs is a fundamental problem used in several application domains. However, existing software frameworks offer suboptimal performance due to high algorithmic complexity and irregular memory accesses of temporal motif mining.This paper presents $\mathsf{Mint}$—a novel accelerator architecture and a programming model for mining temporal motifs efficiently. We first divide this workload into three fundamental tasks: search, book-keeping, and backtracking. Based on this, we propose a task-centric programming model that enables decoupled, asynchronous execution. This model unlocks massive opportunities for parallelism, and allows storing task context information on-chip. To best utilize the proposed programming model, we design a domain-specific hardware accelerator using its data path and memory subsystem design to cater to the unique workload characteristics of temporal motif mining. To further improve performance, we propose a novel optimization called search index memoization that significantly reduces memory traffic. We comprehensively compare the performance of $\mathsf{Mint}$ with state-of-the-art temporal motif mining software frameworks (both approximate and exact) running on both CPU and GPU, and show $9\times-2576\times$ benefit in performance.

Speech privacy leakage has long been a public concern. Through speech eavesdropping, an adversary may steal a user's private information or an enterprise's financial/intellectual properties, leading to catastrophic consequences. Existing non-microphone-based eavesdropping attacks rely on physical contact or line-of-sight between the sensor (e.g., a motion sensor or a radar) and the victim sound source. In this poster, we discover a new form of speech eavesdropping attack that senses minor speech-induced vibrations upon common room objects using mmWave. By integrating phasedarray and multiple-input and multiple-output (MIMO) on a single mmWave transceiver, our attack can capture and fuse micrometerlevel vibrations upon the surfaces of multiple objects to reveal speech content in a remote and non-line-of-sight fashion. We successfully demonstrate such an attack by developing a deep speech recognition scheme grounded on unsupervised domain adaptation. Without prior training on the victim's data, our attack can achieve a high success rate of over 90% in recognizing simple speech content.