Dataflow modeling techniques facilitate many aspects of design exploration and optimization for signal processing systems, such as efficient scheduling, memory management, and task synchronization. The lightweight dataflow (LWDF) programming methodology provides an abstract programming model that supports dataflow-based design and implementation of signal processing hardware and software components and systems. Previous work on LWDF techniques has emphasized their application to DSP software implementation. In this paper, we present new extensions of the LWDF methodology for effective integration with hardware description languages (HDLs), and we apply these extensions to develop efficient methods for low power DSP hardware implementation. Through a case study of a deep neural network application for vehicle classification, we demonstrate our proposed LWDF-based hardware design methodology, and its effectiveness in low power implementation of complex signal processing systems.
Low power design methodology for signal processing systems using lightweight dataflow techniques / Li, Lin; Fanni, Tiziana; Viitanen, Timo; Xie, Renjie; Palumbo, Francesca; Raffo, Luigi; Huttunen, Heikki; Takala, Jarmo; Bhattacharyya, Shuvra S.. - (2017), pp. 82-89. (Intervento presentato al convegno 2016 Conference on Design and Architectures for Signal and Image Processing, DASIP 2016 tenutosi a Rennes, France nel 2016) [10.1109/DASIP.2016.7853801].
Low power design methodology for signal processing systems using lightweight dataflow techniques
PALUMBO, Francesca;
2017-01-01
Abstract
Dataflow modeling techniques facilitate many aspects of design exploration and optimization for signal processing systems, such as efficient scheduling, memory management, and task synchronization. The lightweight dataflow (LWDF) programming methodology provides an abstract programming model that supports dataflow-based design and implementation of signal processing hardware and software components and systems. Previous work on LWDF techniques has emphasized their application to DSP software implementation. In this paper, we present new extensions of the LWDF methodology for effective integration with hardware description languages (HDLs), and we apply these extensions to develop efficient methods for low power DSP hardware implementation. Through a case study of a deep neural network application for vehicle classification, we demonstrate our proposed LWDF-based hardware design methodology, and its effectiveness in low power implementation of complex signal processing systems.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
