David Brooks

About

David Brooks is the Haley Family Professor of Computer Science at Harvard University, affiliated with the Harvard John A. Paulson School of Engineering and Applied Sciences. His primary teaching area is Computer Science. His research areas include applied mathematics, science and engineering for ClimateTech, applied physics, bioengineering, computer engineering and architecture, electrical engineering, environmental science and engineering, materials science, and mechanical engineering. His work involves addressing environmental impacts of computation, with a focus on sustainable computing and reducing the carbon footprint of computing technologies. He is involved in multi-institution research initiatives aimed at advancing green computing solutions.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Computer hardware
• Parallel computing
• Data science
• Operating system
• Computer architecture
• Distributed computing

Selected publications

• The Architectural Implications of Facebook's DNN-Based Personalized Recommendation

  2020 · 259 citations

  The widespread application of deep learning has changed the landscape of computation in data centers. In particular, personalized recommendation for content ranking is now largely accomplished using deep neural networks. However, despite their importance and the amount of compute cycles they consume, relatively little research attention has been devoted to recommendation systems. To facilitate research and advance the understanding of these workloads, this paper presents a set of real-world, pro…

  DOI

• RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing

  2020 · 220 citations

  Personalized recommendation systems leverage deep learning models and account for the majority of data center AI cycles. Their performance is dominated by memory-bound sparse embedding operations with unique irregular memory access patterns that pose a fundamental challenge to accelerate. This paper proposes a lightweight, commodity DRAM compliant, near-memory processing solution to accelerate personalized recommendation inference. The in-depth characterization of production-grade recommendation…

  DOI

• 14.5 A 12nm Linux-SMP-Capable RISC-V SoC with 14 Accelerator Types, Distributed Hardware Power Management and Flexible NoC-Based Data Orchestration

  2024-02-18 · 16 citations

  article

  Modern heterogeneous SoCs feature a mix of many hardware accelerators and general-purpose cores that run many applications in parallel. This brings challenges in managing how the accelerators access shared resources, e.g., the memory hierarchy, communication channels, and on-chip power. We address these challenges through flexible orchestration of data on a 74Tbps network-on-chip (NoC) for dynamic management of the resources under contention and a distributed hardware power management (DHPM) sch…

  PublisherDOI

• GPU-based Private Information Retrieval for On-Device Machine Learning Inference

  2024-04-17 · 10 citations

  article

  On-device machine learning (ML) inference can enable the use of private user data on user devices without revealing them to remote servers. However, a pure on-device solution to private ML inference is impractical for many applications that rely on embedding tables that are too large to be stored on-device. In particular, recommendation models typically use multiple embedding tables each on the order of 1--10 GBs of data, making them impractical to store on-device. To overcome this barrier, we p…

  PublisherDOI

• CORDOBA: Carbon-Efficient Optimization Framework for Computing Systems

  2025-03-01 · 7 citations

  article

  The world’s push toward an environmentally sustainable society is highly dependent on the semiconductor industry. Despite existing carbon modeling efforts to quantify carbon footprint of computing systems, optimizing carbon footprint in large design spaces-while also considering trade-offs in power, performance, and area-is especially challenging. To address this need, we present CORDOBA, a carbon-aware optimization framework that optimizes carbon efficiency. We quantify carbon efficiency using…

  PublisherDOI

Recent grants

• CSR: SMALL: Virtualized Accelerators for Scalable, Composable Architectures

  NSF · $450k · 2017–2022

• NSF CCF-CPA: Reliability in the Face of Variability under Nanoscale Technology Scaling

  NSF · $500k · 2007–2012

• CAREER: A Framework for Early-Stage Computer Architecture Design Space Exploration and Optimization

  NSF · $400k · 2005–2011

Frequent coauthors

• Gu-Yeon Wei

  210 shared

• Udit Gupta

  Harvard University

  45 shared

• Brandon Reagen

  New York University

  43 shared

• Carole-Jean Wu

  38 shared

• Pradip Bose

  IBM (United States)

  38 shared

• Paul N. Whatmough

  28 shared

• Vijay Janapa Reddi

  27 shared

• Mark Hempstead

  24 shared

Similar researchers at Harvard University

• Cynthia Dworkh-78
• Scott E Parsonsh-70
• Yilun Duh-60