SIGOPS

The computational demands of modern AI techniques are immense, and as the number of practical applications grows, there will be an increasing burden on shared computing infrastructure. We envision a forthcoming era of "AI Systems" research where ...

Deploying machine learning into IoT cloud settings will require an evolution of the cloud infrastructure. In this white paper, we justify this assertion and identify new capabilities needed for real-time intelligent systems. We also outline our initial ...

Researchers have proposed hardware, software, and algorithmic optimizations to improve the computational performance of deep learning. While some of these optimizations perform the same operations faster (e.g., increasing GPU clock speed), many others ...

The rapid evolution of deep neural networks is demanding deep learning (DL) frameworks not only to satisfy the requirement of quickly executing large computations, but also to support straightforward programming models for quickly implementing and ...

Performance unpredictability is a major roadblock towards cloud adoption, and has performance, cost, and revenue ramifications. Predictable performance is even more critical as cloud services transition from monolithic designs to microservices. ...

With operating systems being at the core of computer systems, decades of research and engineering efforts have been put into the development of OSes. To keep pace with the speed of modern hardware and application evolvement, we argue that a different ...

Recommendation of items to users is a problem faced by many companies in a wide spectrum of industries. This problem was traditionally approached in a one-shot manner, such as recommending movies to users based on all the movie ratings observed so far. ...

Deep learning (DL) systems expose many tuning parameters ("hyper-parameters") that affect the performance and accuracy of trained models. Increasingly users struggle to configure hyper-parameters, and a substantial portion of time is spent tuning them ...

Deep learning (DL) systems are increasingly deployed in safety- and security-critical domains including autonomous driving, robotics, and malware detection, where the correctness and predictability of a system on corner-case inputs are of great ...

While decision-makings in systems are commonly solved with explicit rules and heuristics, machine learning (ML) and deep learning (DL) have been driving a paradigm shift in modern system design. Based on our decade of experience in operationalizing a ...

We present Sage, the first ML platform that enforces a global differential privacy (DP) guarantee across all models produced from a sensitive data stream. Sage extends the Tensorflow-Extended ML platform with novel mechanisms and DP theory to address ...

The data gluttony of AI is well known: Data fuels the artificial intelligence. Technologies that help to gather the needed data are then essential, among which the IoT. However, the deployment of IoT solutions raises significant challenges, especially ...