PiDRAM: A Holistic End-to-end FPGA-based Framework for Processing-in-DRAM

TL;DR

PiDRAM is a comprehensive FPGA-based framework that facilitates the integration and evaluation of processing-in-DRAM techniques, demonstrating significant performance improvements for bulk memory operations and enabling easy implementation of in-memory random number generation.

Contribution

It introduces PiDRAM, the first flexible end-to-end system for integrating and evaluating processing-in-DRAM techniques across hardware and software layers.

Findings

In-memory copy and initialization improve bulk operation performance by over 12x and 14x.

PiDRAM enables rapid development of PuM techniques with minimal hardware and software code.

The framework supports real-system evaluation of PuM methods on FPGA-based platforms.

Abstract

Processing-using-memory (PuM) techniques leverage the analog operation of memory cells to perform computation. Several recent works have demonstrated PuM techniques in off-the-shelf DRAM devices. Since DRAM is the dominant memory technology as main memory in current computing systems, these PuM techniques represent an opportunity for alleviating the data movement bottleneck at very low cost. However, system integration of PuM techniques imposes non-trivial challenges that are yet to be solved. Design space exploration of potential solutions to the PuM integration challenges requires appropriate tools to develop necessary hardware and software components. Unfortunately, current specialized DRAM-testing platforms, or system simulators do not provide the flexibility and/or the holistic system view that is necessary to deal with PuM integration challenges. We design and develop PiDRAM, the first flexible end-to-end framework that enables system integration studies and evaluation of real PuM techniques. PiDRAM provides software and hardware components to rapidly integrate PuM techniques across the whole system software and hardware stack (e.g., necessary modifications in the operating system, memory controller). We implement PiDRAM on an FPGA-based platform along with an open-source RISC-V system. Using PiDRAM, we implement and evaluate two state-of-the-art PuM techniques: in-DRAM (i) copy and initialization, (ii) true random number generation. Our results show that the in-memory copy and initialization techniques can improve the performance of bulk copy operations by 12.6x and bulk initialization operations by 14.6x on a real system. Implementing the true random number generator requires only 190 lines of Verilog and 74 lines of C code using PiDRAM's software and hardware components.