Classification and transformation of dynamic dataflow programs

International audienceDataflow programming has been used to describe signal processing applications for many years, traditionally with cyclostatic dataflow (CSDF) or synchronous dataflow (SDF) models that restrict expressive power in favor of compile-time analysis and predictability. Dynamic dataflow is not restricted with respect to expressive power, but it does require runtime scheduling in the general case. Fortunately, most signal processing applications are far from being entirely dynamic, and parts with static behavior need not be dynamically scheduled. This paper presents a method to automatically analyze and classify blocks of a dynamic dataflow program within more restrictive dataflow models when possible, and to transform the blocks classified as static to improve execution speed by reducing the number of FIFO accesses. We used this method on actors of two dynamic dataflow descriptions of an MPEG-4 part 2 decoder, and study how classification and transformation increases decoding speed

Relay: A New IR for Machine Learning Frameworks

Machine learning powers diverse services in industry including search, translation, recommendation systems, and security. The scale and importance of these models require that they be efficient, expressive, and portable across an array of heterogeneous hardware devices. These constraints are often at odds; in order to better accommodate them we propose a new high-level intermediate representation (IR) called Relay. Relay is being designed as a purely-functional, statically-typed language with the goal of balancing efficient compilation, expressiveness, and portability. We discuss the goals of Relay and highlight its important design constraints. Our prototype is part of the open source NNVM compiler framework, which powers Amazon's deep learning framework MxNet

Classification of Dataflow Actors with Satisfiability and Abstract Interpretation

International audienceDataflow programming has been used to describe signal processing applications for many years, traditionally with cyclo-static dataflow (CSDF) or synchronous dataflow (SDF) models that restrict expressive power in favor of compile-time analysis and predictability. More recently, dynamic dataflow is being used for the description of multimedia video standards as promoted by the RVC standard (ISO/IEC 23001:4). Dynamic dataflow is not restricted with respect to expressive power, but it does require runtime scheduling in the general case, which may be costly to perform on software. The authors presented in a previous paper a method to automatically classify actors of a dynamic dataflow program within more restrictive dataflow models when possible, along with a method to transform the actors classified as static to improve execution speed by reducing the number of FIFO accesses (Wipliez & Raulet, 2010). This paper presents an extension of the classification method using satisfiability solving, and details the precise semantics used for the abstract interpretation of actors. The extended classification is able to classify more actors than what could previously be achieved

Static Analysis and Transformation of Dataflow Multimedia Applications

An approach for merging statically schedulable subr egions in dataflow models is pr esented. The approach combines abstr act int erpr etation, loop analysis, and static scheduling of cyclo-static dataflow networ ks. The approach has been implemented in a Java-based tool that per forms automatic classification of dataflow act or s, generat ion of stat ic schedules using constr aint programming, and automatic merging of the finegrained act or s in the subnetwor k into a single, larger -grained actor . The approach is applied to an MPEG-4 SP video decoder implemented in the dataflow act or s language CAL