15 citations · 37 across the 13 of their papers we have counts for
18 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…
Full System Architecture Modeling for Wearable Egocentric Contextual AI
Vincent T. Lee, Tanfer Alan, Sung Kim +6
The next generation of human-oriented computing will require always-on, spatially-aware wearable devices to capture egocentric vision and functional primitives (e.g., Where am I? W…
Verifiable Access Control for Augmented Reality Localization and Mapping
Shaowei Zhu, Hyo Jin Kim, Maurizio Monge +4
Localization and mapping is a key technology for bridging the virtual and physical worlds in augmented reality (AR). Localization and mapping works by creating and querying maps ma…
Homomorphically Encrypted Computation using Stochastic Encodings
Hsuan Hsiao, Vincent Lee, Brandon Reagen +1
Homomorphic encryption (HE) is a privacy-preserving technique that enables computation directly over ciphertext. Unfortunately, a key challenge for HE is that implementations can b…
HWTool: Fully Automatic Mapping of an Extensible C++ Image Processing Language to Hardware
James Hegarty, Omar Eldash, Amr Suleiman +1
Implementing image processing algorithms using FPGAs or ASICs can improve energy efficiency by orders of magnitude over optimized CPU, DSP, or GPU code. These efficiency improvemen…
Analysis and Mitigations of Reverse Engineering Attacks on Local Feature Descriptors
Deeksha Dangwal, Vincent T. Lee, Hyo Jin Kim +9
As autonomous driving and augmented reality evolve, a practical concern is data privacy. In particular, these applications rely on localization based on user images. The widely ado…
SoK: Opportunities for Software-Hardware-Security Codesign for Next Generation Secure Computing
Deeksha Dangwal, Meghan Cowan, Armin Alaghi +3
Users are demanding increased data security. As a result, security is rapidly becoming a first-order design constraint in next generation computing systems. Researchers and practit…
Neural Network Compression for Noisy Storage Devices
Berivan Isik, Kristy Choi, Xin Zheng +4
Compression and efficient storage of neural network (NN) parameters is critical for applications that run on resource-constrained devices. Despite the significant progress in NN mo…
Porcupine: A Synthesizing Compiler for Vectorized Homomorphic Encryption
Meghan Cowan, Deeksha Dangwal, Armin Alaghi +3
Homomorphic encryption (HE) is a privacy-preserving technique that enables computation directly on encrypted data. Despite its promise, HE has seen limited use due to performance o…
Synthesizing Number Generators for Stochastic Computing using Mixed Integer Programming
Vincent T. Lee, Samuel Archibald Elliot, Armin Alaghi +1
Stochastic computing (SC) is a high density, low-power computation technique which encodes values as unary bitstreams instead of binary-encoded (BE) values. Practical SC implementa…
Stochastic Synthesis for Stochastic Computing
Vincent T. Lee, Armin Alaghi, Luis Ceze +1
Stochastic computing (SC) is an emerging computing technique which offers higher computational density, and lower power over binary-encoded (BE) computation. Unlike BE computation,…
Exploiting Errors for Efficiency: A Survey from Circuits to Algorithms
Phillip Stanley-Marbell, Armin Alaghi, Michael Carbin +13
When a computational task tolerates a relaxation of its specification or when an algorithm tolerates the effects of noise in its execution, hardware, programming languages, and sys…
Correlation Manipulating Circuits for Stochastic Computing
Vincent T. Lee, Armin Alaghi, Luis Ceze
Stochastic computing (SC) is an emerging computing technique that promises high density, low power, and error tolerant solutions. In SC, values are encoded as unary bitstreams and…
Energy-Efficient Hybrid Stochastic-Binary Neural Networks for Near-Sensor Computing
Vincent T. Lee, Armin Alaghi, John P. Hayes +2
Recent advances in neural networks (NNs) exhibit unprecedented success at transforming large, unstructured data streams into compact higher-level semantic information for tasks suc…
MATIC: Learning Around Errors for Efficient Low-Voltage Neural Network Accelerators
Sung Kim, Patrick Howe, Thierry Moreau +3
As a result of the increasing demand for deep neural network (DNN)-based services, efforts to develop dedicated hardware accelerators for DNNs are growing rapidly. However,while ac…
Exploring Computation-Communication Tradeoffs in Camera Systems
Amrita Mazumdar, Thierry Moreau, Sung Kim +5
Cameras are the defacto sensor. The growing demand for real-time and low-power computer vision, coupled with trends towards high-efficiency heterogeneous systems, has given rise to…
Similarity Search on Automata Processors
Vincent T. Lee, Justin Kotalik, Carlo C. Del Mundo +3
Similarity search is a critical primitive for a wide variety of applications including natural language processing, content-based search, machine learning, computer vision, databas…
Application-Driven Near-Data Processing for Similarity Search
Vincent T. Lee, Amrita Mazumdar, Carlo C. del Mundo +3
Similarity search is a key to a variety of applications including content-based search for images and video, recommendation systems, data deduplication, natural language processing…