EVE: A Flexible SIMD Coprocessor for Embedded Vision Applications

Abstract

In this paper we introduce EVE (embedded vision/vector engine), with a FlexSIMD (flexible SIMD) architecture highly optimized for embedded vision. We show how EVE can be used to meet the growing requirements of embedded vision applications in a power- and area-efficient manner. EVE's SIMD features allow it to accelerate low-level vision functions (such as image filtering, color-space conversion, pyramids, and gradients). With added flexibility of data accesses, EVE can also be used to accelerate many mid-level vision tasks (such as connected components, integral image, histogram, and Hough transform). Our experiments with a silicon implementation of EVE show that it performs many low- and mid-level vision functions with a 3---12x speed advantage over a C64x+DSP, while consuming less power and area. EVE also achieves code size savings of 4---6x over a C64x+DSP for regular loops. Thanks to its flexibility and programmability, we were able to implement two end-to-end vision applications on EVE and achieve more than a 5× application-level speedup over a C64x+. Having EVE as a coprocessor next to a DSP or a general purpose processor, algorithm developers have an option to accelerate the low- and mid-level vision functions on EVE. This gives them more room to innovate and use the DSP for new, more complex, high-level vision algorithms.