A 34.1fps scale-space processor with two-dimensional cache for real-time object recognition

A scale-space processor with two-dimensional cache is proposed to achieve real-time object recognition in HD 720p images. Scale-space is the most commonly used concept to achieve scale-invariant property in object recognition, however its high computational cost makes it hard to implement a realtime object recognition processor. We employ hierarchical convolution unit (HCU) which computes multiple pixels in a single cycle with various kernel sizes. In addition, two-dimensional cache (T-Cache) supports accessing vertically consecutive data from any image window size with reduced area. A pre-fetch controller for the proposed cache improves the hit rate by exploiting the sequential access pattern of convolution tasks. As a result, the scale-space processor implemented in a 0.13μm CMOS technology achieves 34.1fps on a HD 720p image while consuming peak power of 84.5mW.