APPROX 2014 - Workshop on Probabilistic and Approximate Computing

Research is increasingly focusing on computing in the presence of approximation and inexactness, partly due to inexact data (for example, from sensors or from machine learning methods), and partly due to the performance and power benefits that arise from deliberate use of approximation. These methods require new approaches to every aspect of the hardware and software stack, ranging from new hardware to new algorithms to new languages and formal methods.
This workshop is an effort to bring together constituents from across these diverse areas to discuss challenges, opportunities, abstractions, and foundations. This research area has the additional exciting aspect that substantive research contributions often require diverse participation from research areas that include architecture, programming languages, machine learning, and distributed systems. Our goal is to bring together members of these diverse communities and build a shared understanding of concepts, applications, foundations, and systems.
Topics considered in-scope for the workshop include:
Mechanisms for approximation in hardware and software
Abstractions for approximation and uncertainty in programs (PL support)
Performance and efficiency improvements based on approximation
Domain-specific solutions using approximate computing
Domains in which approximation and noisy data is the norm, such as medical and sensor data
Incorporating results from machine learning as approximations in programs
Formal reasoning about programs with approximations
Important applications that allow approximations
Concurrency and approximation
Approximation and privacy/security
Compiler optimizations in the presence of approximate computing
Clean abstractions for describing and using machine-learning techniques in programs