Analogic CNN Engine for Muli-Spectral Fusion

TeraOps Corporation proposes to develop a suite of algorithmic techniques and corresponding hardware implementation based on the powerful CNN technology to address Navy requirements for multi-band image fusion in real-time. TeraOps will exploit the unique architecture of the CNN supercomputer-on-a-chip to implement spatial-temporal image processing algorithms for the fusion of multi-band sensory data to be used in advanced targeting systems. These algorithms will draw upon studies of biological image processing systems as well as on CNN models for color constancy and color visualization developed earlier by TearOps researchers. CNN is a massively parallel locally interconnected dynamic array computer capable of performing more than 10 to the 12 operations per second which allows it to perform image processing operations three orders of magnitude faster than conventional digital signal processors. Its very high processing speed is essential in executing computationally intensive image processing operations in real-time at high resolutions. The CNN image processing package including associated hardware will be small enough to fit in the limited space of an image processing pod and will consume very little power due to its highly efficient VLSI implementation. Thus, the proposed work combines the efficiency and speed of the hardware implementation and the algorithmic power of CNN technology to solve the real-time image fusion problem.