Toolflow for the algorithm-hardware co-design of memristive ANN accelerators

Abstract

The capabilities of artificial neural networks are rapidly evolving, so are the expectations for them to solve ever more challenging tasks in numerous everyday situations. Larger, more complex networks and the need to execute them efficiently on edge devices are the two counteracting requirements of this trend. Novel devices and computation techniques show promising characteristics to address this challenge. A huge design space covering different combinations of neural networks and hardware architectures using these technologies needs to be explored. An efficient design flow is, therefore, crucial for a good quality of service. This work reviews a wide range of simulation tools for novel memristive devices and analyzes their applicability for the design space exploration. A modular toolflow is proposed that shrinks down the large design space step-by-step using state-of-the-art optimization techniques and builds upon existing tools to find the best trade-offs between network accuracy and hardware requirements.

Keywords

• Memristive crossbars
• Processing-in-memory
• Approximate computing
• Toolflow
• Design space exploration