A Machine Learning Imaging Core using Separable FIR-IIR Filters
This work addresses the need for low-power, area-efficient image processing in mobile devices, though it is incremental as it builds on existing neural network and filter techniques.
The authors tackled the problem of efficient pixel-to-pixel image transformations for mobile devices by proposing a fixed-function neural network hardware called MagIC, which uses separable FIR-IIR filters to achieve a silicon area of ~3mm², consumes 56mW at 500MHz, and delivers 23TOPS/W/mm² throughput.
We propose fixed-function neural network hardware that is designed to perform pixel-to-pixel image transformations in a highly efficient way. We use a fully trainable, fixed-topology neural network to build a model that can perform a wide variety of image processing tasks. Our model uses compressed skip lines and hybrid FIR-IIR blocks to reduce the latency and hardware footprint. Our proposed Machine Learning Imaging Core, dubbed MagIC, uses a silicon area of ~3mm^2 (in TSMC 16nm), which is orders of magnitude smaller than a comparable pixel-wise dense prediction model. MagIC requires no DDR bandwidth, no SRAM, and practically no external memory. Each MagIC core consumes 56mW (215 mW max power) at 500MHz and achieves an energy-efficient throughput of 23TOPS/W/mm^2. MagIC can be used as a multi-purpose image processing block in an imaging pipeline, approximating compute-heavy image processing applications, such as image deblurring, denoising, and colorization, within the power and silicon area limits of mobile devices.