Ross Daly

Computer Science Ph.D.
Advisor: Pat Hanrahan
rdaly525 at stanford dot edu


CoreIR: An Intermediate Representation and Compiler Framework


Rigel: Flexible Multi-Rate Image Processing Hardware
James Hegarty, Ross Daly, Zachary DeVito, Jonathan Ragan-Kelley, Mark Horowitz, Pat Hanrahan

Image processing algorithms implemented using custom hardware or FPGAs of can be orders-of-magnitude more energy efficient and performant than software. In this paper, we present Rigel, which takes pipelines specified in our new multi-rate architecture and lowers them to FPGA implementations. Our flexible multi-rate architecture builds on our prior Darkroom system to support pyramid image processing, sparse computations, and space-time implementation tradeoffs. We demonstrate depth from stereo, Lucas-Kanade, the SIFT descriptor, and a Gaussian pyramid running on two FPGA boards. Our system can synthesize hardware for FPGAs with up to 436 Megapixels/second throughput, and up to 297X faster runtime than a tablet-class ARM CPU.

Flipping Bits in Memory Without Accessing Them: An Experimental Study of DRAM Disturbance Errors
Yoongu Kim, Ross Daly, Jeremie Kim, Chris Fallin, Ji Hye Lee, Donghyuk Lee, Chris Wilkerson, Konrad Lai, Onur Mutlu
ISCA 2014

Memory isolation is a key property of a reliable and secure computing system: an access to one memory address should not have unintended side effects on data stored in other addresses. However, as DRAM process technology scales down to smaller dimensions, it becomes more difficult to prevent DRAM cells from electrically interacting with each other. In this paper, we expose the vulnerability of commodity DRAM chips to disturbance errors. By reading from the same address in DRAM, we show that it is possible to corrupt data in nearby addresses. More specifically, activating the same row in DRAM corrupts data in nearby rows. We demonstrate this phenomenon on Intel and AMD systems using a malicious program that generates many DRAM accesses.


Hardware Programming Languages and Tools
Computer Vision and Machine Learning
Accelerators and Heterogeneous Computing