In the existing near-/in-sensor computing architectures for vision tasks, the affect of the image signal processing (ISP) pipeline, which is of great importance to the final vision performance, is always ignored. In this work, we propose a synthesized RAW image-based end-to-end computer vision paradigm, taking the affect of ISP pipeline into account. Experimental results show that by training/tuning the CNN models using synthesized RAW images, it is possible to design an end-to-end (from RAW image to vision task) vision system that directly consumes RAW image data from the sensor with negligible vision performance degradation. By skipping the ISP pipeline, an image sensor can be directly integrated with the back-end vision processor without a complex image processor in the middle, making near-/in-sensor computing a practical approach.