C4CAM: A Compiler for CAM-based In-memory Accelerators

TL;DR

C4CAM is a novel compiler framework that automates the generation of code for CAM-based in-memory accelerators from high-level TorchScript, enabling efficient exploration of configurations and performance-energy trade-offs.

Contribution

It introduces the first compiler framework for CAM-based accelerators, facilitating high-level programming and configuration exploration for in-memory computing systems.

Findings

Enables rapid exploration of CAM configurations.

Analyzes performance and energy impacts of different CAM technologies.

Supports high-level programming for CAM-based accelerators.

Abstract

Machine learning and data analytics applications increasingly suffer from the high latency and energy consumption of conventional von Neumann architectures. Recently, several in-memory and near-memory systems have been proposed to remove this von Neumann bottleneck. Platforms based on content-addressable memories (CAMs) are particularly interesting due to their efficient support for the search-based operations that form the foundation for many applications, including K-nearest neighbors (KNN), high-dimensional computing (HDC), recommender systems, and one-shot learning among others. Today, these platforms are designed by hand and can only be programmed with low-level code, accessible only to hardware experts. In this paper, we introduce C4CAM, the first compiler framework to quickly explore CAM configurations and to seamlessly generate code from high-level TorchScript code. C4CAM employs a hierarchy of abstractions that progressively lowers programs, allowing code transformations at the most suitable abstraction level. Depending on the type and technology, CAM arrays exhibit varying latencies and power profiles. Our framework allows analyzing the impact of such differences in terms of system-level performance and energy consumption, and thus supports designers in selecting appropriate designs for a given application.