Kent D. Moat

Streaming processors for next-generation mobile imaging applications

Next-generation mobile devices will continue to demand high processing power for imaging applications. The expected performance is in the class of supercomputers, but delivered with limited energy and memory bandwidth for embedded systems. This article advocates a streaming computation model that leverages the deterministic access patterns in imaging applications to deliver the necessary processing throughput. A reconfigurable datapath connects a set of functional units, forming a computation pipeline to offer energy efficiency. The architecture and implementation of a stream processor are presented along with the memory subsystem to support stream data transfers. The results show speedup ranging from a factor of 2 to 28 for imaging applications, offering favorable comparison against scalar processors.

RSVP II: a next generation automotive vector processor

A large number of sensors (i.e., video, radar, laser, ultrasound, etc.) that continuously monitor the environment are finding their way in the average automobile. The algorithms processing the data captured by these sensors are streaming in nature and require a high rate of computation. Due to the characteristics of the automotive environment, this computation has to be delivered under very low energy and cost budgets. The reconfigurable streaming vector processing (RSVP/spl trade/) architecture is a vector coprocessor architecture which accelerates streaming data processing. This paper presents the RSVP architecture and its second implementation, RSVP II. Our results show significant speedups on data streaming functions running compiled code. On a lane tracking application, RSVP II shows impressive performance results. From a performance/

RSVP/spl trade/: an automotive vector processor

and performance/mW perspective, RSVP architecture compares favorably with leading DSP architectures. The time to market is substantially reduced due to ease of programmability, elimination of hand-tuned assembly code, and support for software re-use through binary compatibility across multiple implementations.

The Reconfigurable Streaming Vector Processor (RSVPTM)

A myriad of sensors (i.e., video, radar, laser, ultrasound, etc.) continuously monitoring the environment are incorporated in future automobiles. The algorithms processing the data captured by these sensors are streaming in nature and require high levels of processing power. Due to the characteristics of the automotive market, this processing power has to be delivered under very low energy and cost budgets. The Reconfigurable Streaming Vector Processing (RSVP/spl trade/) is a vector coprocessor architecture which accelerates streaming data processing. This paper presents the RSVP architecture, programming model, and a first implementation. Our results show significant speedups on data streaming functions. Running compiled code, RSVP outperforms an ARM9 host processor on average by a factor of 31 on a set of kernels. From a performance/

Streaming vector processor with reconfigurable interconnection switch

and performance/mW perspective, RSVP compares favorably with leading DSP architectures. The time to market is substantially reduced due to ease of programmability, elimination of hand-tuned assembly code, and support for software re-use through binary compatibility across multiple implementations.