ReVAMP: ReRAM based VLIW architecture for in-memory computing.

With diverse types of emerging devices offering simultaneous capability of storage and logic operations, researchers have proposed novel platforms that promise gains in energy-efficiency. Such platforms can be classified into two domains---application-specific and general-purpose. The application-specific in-memory computing platforms include machine learning accelerators, arithmetic units, and Content Addressable Memory (CAM)-based structures. On the other hand, the general-purpose computing platforms stem from the idea that several in-memory computing logic devices do support a universal set of Boolean logic operation and therefore, can be used for mapping arbitrary Boolean functions efficiently. In this direction, so far, researchers have concentrated on challenges in logic synthesis (e.g. depth optimization), and technology mapping (e.g. device count reduction). The important problem of efficient technology mapping of arbitrary logic network onto a crossbar array structure has been overlooked so far. In this paper, we propose, ReVAMP, a general-purpose computing platform based on Resistive RAM crossbar array, which exploits the parallelism in computing multiple logic operations in the same word. Further, we study the problem of instruction generation and scheduling for such a platform. We benchmark the performance of ReVAMP with respect to the state of the art architecture.