A Machine Learning Imaging Core using Separable FIR-IIR Filters

TL;DR

This paper introduces MagIC, a compact, energy-efficient neural network hardware core for real-time image processing tasks, optimized for mobile devices with minimal power and silicon area.

Contribution

The paper presents a novel fixed-function neural network hardware core using hybrid FIR-IIR filters, achieving high efficiency and small silicon footprint for diverse image processing applications.

Findings

MagIC core occupies ~3mm^2 silicon area in 16nm process.

Achieves 23 TOPS/W/mm^2 energy efficiency at 500MHz.

Supports multiple image processing tasks without external memory.

Abstract

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.