ASPLOS

Accurate disassembly of stripped binaries is the first step in binary analysis, instrumentation and reverse engineering. Complex instruction sets such as the x86 pose major challenges in this context because it is very difficult to distinguish between ...

We introduce the Bayesian Compiler Optimization framework (BaCO), a general purpose autotuner for modern compilers targeting CPUs, GPUs, and FPGAs. BaCO provides the flexibility needed to handle the requirements of modern autotuning tasks. Particularly, ...

Today's cloud platforms offer large virtual machine (VM) instances with multiple virtual CPUs (vCPU) on manycore machines. These machines typically have a deep memory hierarchy to enhance communication between cores. Although previous researches have ...

Serverless computing that runs functions with auto-scaling is a popular task execution pattern in the cloud-native era. By connecting serverless functions into workflows, tenants can achieve complex functionality. Prior research adopts the control-flow ...

Emerging real-time multi-model ML (RTMM) workloads such as AR/VR and drone control involve dynamic behaviors in various granularity; task, model, and layers within a model. Such dynamic behaviors introduce new challenges to the system software in an ML ...

Effective design space exploration (DSE) is paramount for hardware/software codesigns of deep learning accelerators that must meet strict execution constraints. For their vast search space, existing DSE techniques can require excessive trials to obtain a ...

A critical feature in today's quantum circuit is that they have permutable two-qubit operators. The flexibility in ordering the permutable two-qubit gates leads to more compiler optimization opportunities. However, it also imposes significant challenges ...

Modern vector processors support a wide variety of instructions for fixed-point digital signal processing. These instructions support a proliferation of rounding, saturating, and type conversion modes, and are often fused combinations of more primitive ...

Finding vulnerabilities in firmware is vital as any firmware vulnerability may lead to cyberattacks to the physical IoT devices. Taint analysis is one promising technique for finding firmware vulnerabilities thanks to its high coverage and scalability. ...

Caching function is a promising way to mitigate coldstart overhead in serverless computing. However, as caching also increases the resource cost significantly, how to make caching decisions is still challenging. We find that the prior "local cache ...

Data processing oriented software, especially machine learning applications, are heavily dependent on standard frameworks/libraries such as TensorFlow and OpenCV. As those frameworks have gained significant popularity, the exploitation of vulnerabilities ...

The emergence of machine learning, image and audio processing on edge devices has motivated research towards power-efficient custom hardware accelerators. Though FPGAs are an ideal target for custom accelerators, the difficulty of hardware design and the ...

To lower the barrier to diffractive optical neural networks (DONNs) design, exploration, and deployment, we propose LightRidge, the first end-to-end optical ML compilation framework, which consists of (1) precise and differentiable optical physics ...

The demise of Moore's Law and Dennard Scaling has revived interest in specialized computer architectures and accelerators. Verification and testing of this hardware depend heavily upon cycle-accurate simulation of register-transfer-level (RTL) designs. ...

We present a new approach to static memory allocation, a key problem that arises in the compilation of machine learning models onto the resources of a specialized hardware accelerator. Our methodology involves a recursive depth-first search that limits ...

With the continuous improvement of on-chip integrated voltage regulators (IVRs) and fast, adaptive frequency control, dynamic voltage-frequency scaling (DVFS) transition times have shrunk from the microsecond to the nanosecond regime, providing immense ...

Embedding columns are important for deep recommendation models to achieve high accuracy, but they can be very time-consuming during inference. Machine learning (ML) compilers are used broadly in real businesses to optimize ML models automatically. ...

Years of experience in operating large-scale microservice systems strengthens our belief that their individual components, with inevitable anomalies, still demand a quantification of the influences on the end-to-end performance indicators. On a causal ...

Cloud microservices are being scaled up due to the rising demand for new features and the convenience of cloud-native technologies. However, the growing scale of microservices complicates the remote procedure call (RPC) dependency graph, exacerbates the ...

Harnessing the power of SIMD can bring tremendous performance gains in data processing. In querying streamed JSON data, the state of the art leverages SIMD to fast forward significant portions of the document. However, it does not provide support for ...

For potential quantum advantage, Variational Quantum Algorithms (VQAs) need high accuracy beyond the capability of today's NISQ devices, and thus will benefit from error mitigation. In this work we are interested in mitigating measurement errors which ...

Confidential virtual machines (CVMs) enabled by AMD SEV provide a protected environment for sensitive computations on an untrusted cloud. Unfortunately, CVMs are typically deployed with huge and vulnerable operating system kernels, exposing the CVMs to ...

The metadata service (MDS) sits on the critical path for distributed file system (DFS) operations, and therefore it is key to the overall performance of a large-scale DFS. Common "serverful" MDS architectures, such as a single server or cluster of ...