182 citations · 277 across the 8 of their papers we have counts for
9 papers
Academia x Industry: The Role of Fundamentals for Silicon in an AI Native Era
Vincent T. Lee, Armin Alaghi, Carole-Jean Wu +5
Agentic AI is set to become one of the most transformational technologies in generations and materially change how we approach silicon design and engineering. The impact is being f…
Impala: Low-Latency, Communication-Efficient Private Deep Learning Inference
Woo-Seok Choi, Brandon Reagen, Gu-Yeon Wei +1
This paper proposes Impala, a new cryptographic protocol for private inference in the client-cloud setting. Impala builds upon recent solutions that combine the complementary stren…
Cheetah: Optimizing and Accelerating Homomorphic Encryption for Private Inference
Brandon Reagen, Wooseok Choi, Yeongil Ko +4
As the application of deep learning continues to grow, so does the amount of data used to make predictions. While traditionally, big-data deep learning was constrained by computing…
DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inference
Udit Gupta, Samuel Hsia, Vikram Saraph +6
Neural personalized recommendation is the corner-stone of a wide collection of cloud services and products, constituting significant compute demand of the cloud infrastructure. Thu…
RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing
Liu Ke, Udit Gupta, Carole-Jean Wu +18
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 embed…
MASR: A Modular Accelerator for Sparse RNNs
Udit Gupta, Brandon Reagen, Lillian Pentecost +5
Recurrent neural networks (RNNs) are becoming the de facto solution for speech recognition. RNNs exploit long-term temporal relationships in data by applying repeated, learned tran…
The Architectural Implications of Facebook's DNN-based Personalized Recommendation
Udit Gupta, Carole-Jean Wu, Xiaodong Wang +12
The widespread application of deep learning has changed the landscape of computation in the data center. In particular, personalized recommendation for content ranking is now large…
Weightless: Lossy Weight Encoding For Deep Neural Network Compression
Brandon Reagen, Udit Gupta, Robert Adolf +4
The large memory requirements of deep neural networks limit their deployment and adoption on many devices. Model compression methods effectively reduce the memory requirements of t…
Fathom: Reference Workloads for Modern Deep Learning Methods
Robert Adolf, Saketh Rama, Brandon Reagen +2
Deep learning has been popularized by its recent successes on challenging artificial intelligence problems. One of the reasons for its dominance is also an ongoing challenge: the n…