Feature description is an essential component of many computer vision applications. It encodes the visual contents of images in a manner that is robust against various image transformations. Computing these descriptors is computationally expensive, which causes a performance bottleneck in many embedded vision systems. Although many hardware architectures have been proposed to accelerate feature description computation, most target a single feature description algorithm under specific constraints. The lack of flexibility of such implementations increases development effort if deployed applications need to be modified or upgraded. In this paper, we propose a software configurable hardware architecture capable of computing different types of histogram-based feature descriptors without the need for re-synthesizing the hardware. The architecture takes advantage of data streaming to reduce the computational complexity of computing this class of descriptor. To illustrate the efficiency of our architecture, we deploy two of the most commonly used descriptors (SIFT and HOG) and compare their quality with software implementations. The architecture is also evaluated in terms of execution speed and resource usage and compared with dedicated hardware architectures. Our flexible architecture shows a speed up of 3x and 5x compared to state-of-the-art dedicated hardware architectures for SIFT and HOG, with resource usage overheads [LUTs, FFs, and DSPs] of [1.1x, 15x, and 1.6x] and [6.4x, 7x, and 32x] for SIFT and HOG, respectively.