arXiv subjects

Vincent T. Lee

Publications and source records attributed to Vincent T. Lee.

12 recordsLinked to original sources

arXiv2026

Academia x Industry: The Role of Fundamentals for Silicon in an AI Native Era

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 felt in real time amid a rapidly changing landscape, which can make it overwhelming for both silicon practitioners and academics to adapt to the AI native silicon design era. To add structure to how we navigate this transition, we provide a joint view from academia and industry silicon practitioners of the challenges, opportunities, and considerations we expect will catalyze how the community transitions into an AI native silicon future. In particular, we reemphasize the importance of core silicon design fundamentals in academic training and why they have renewed importance in research and industry practice for AI native silicon design. It is our hope that the views provided here will offer valuable and complementary perspectives to those in academia and industry to interpret, inform, and catalyze the transition to the AI native era. We expect that many similar and overlapping views will emerge, but the precise technical details will differ across stakeholders, so it is valuable for the community to amass a diversity of viewpoints.

cs.AR↗

arXiv2026

Architecture Carbon Tool v3: Enabling Sustainability-aware Silicon System Design Exploration

As the carbon cost of manufacturing and operating semiconductor devices has come into sharper focus, sustainability has gradually emerged as a new system architecture design metric. Like power and performance modeling tools, enabling sustainability-aware silicon systems design and optimizations will require a new generation of electronic design automation and architectural modeling tools. Towards this end, we present an update to the Architecture Carbon Tool v3 (ACT3) which aims to provide an extensible and customizable modeling platform for research and advanced development to pave the path towards sustainability-aware architectural design space exploration. Compared to previous versions of ACT, ACT3 provides significantly richer modeling capabilities, enhanced collateral and analysis telemetry, and first order design space exploration capabilities. This technical brief provides an overview of these expanded capabilities and illustrates ACT3's basic utility across several case studies. Finally, we identify opportunities for research and development where we expect the research community can contribute towards continuing to improve sustainability modeling and design methodology for silicon systems.

cs.AR↗

arXiv2025

Full System Architecture Modeling for Wearable Egocentric Contextual AI

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? What am I looking at?, etc.). These devices will sense an egocentric view of the world around us to observe all human-relevant signals across space and time to construct and maintain a user's personal context. This personal context, combined with advanced generative AI, will unlock a powerful new generation of contextual AI personal assistants and applications. However, designing a wearable system to support contextual AI is a daunting task because of the system's complexity and stringent power constraints due to weight and battery restrictions. To understand how to guide design for such systems, this work provides the first complete system architecture view of one such wearable contextual AI system (Aria2), along with the lessons we have learned through the system modeling and design space exploration process. We show that an end-to-end full system model view of such systems is vitally important, as no single component or category overwhelmingly dominates system power. This means long-range design decisions and power optimizations need to be made in the full system context to avoid running into limits caused by other system bottlenecks (i.e., Amdahl's law as applied to power) or as bottlenecks change. Finally, we reflect on lessons and insights for the road ahead, which will be important toward eventually enabling all-day, wearable, contextual AI systems.

cs.AR↗

arXiv2021

Analysis and Mitigations of Reverse Engineering Attacks on Local Feature Descriptors

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 adopted technology uses local feature descriptors, which are derived from the images and it was long thought that they could not be reverted back. However, recent work has demonstrated that under certain conditions reverse engineering attacks are possible and allow an adversary to reconstruct RGB images. This poses a potential risk to user privacy. We take this a step further and model potential adversaries using a privacy threat model. Subsequently, we show under controlled conditions a reverse engineering attack on sparse feature maps and analyze the vulnerability of popular descriptors including FREAK, SIFT and SOSNet. Finally, we evaluate potential mitigation techniques that select a subset of descriptors to carefully balance privacy reconstruction risk while preserving image matching accuracy; our results show that similar accuracy can be obtained when revealing less information.

cs.CV↗

arXiv2021

SoK: Opportunities for Software-Hardware-Security Codesign for Next Generation Secure Computing

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 practitioners are exploring various security technologies to meet user demand such as trusted execution environments (e.g., Intel SGX, ARM TrustZone), homomorphic encryption, and differential privacy. Each technique provides some degree of security, but differs with respect to threat coverage, performance overheads, as well as implementation and deployment challenges. In this paper, we present a systemization of knowledge (SoK) on these design considerations and trade-offs using several prominent security technologies. Our study exposes the need for \textit{software-hardware-security} codesign to realize efficient and effective solutions of securing user data. In particular, we explore how design considerations across applications, hardware, and security mechanisms must be combined to overcome fundamental limitations in current technologies so that we can minimize performance overhead while achieving sufficient threat model coverage. Finally, we propose a set of guidelines to facilitate putting these secure computing technologies into practice.

cs.CR↗

arXiv2021

Porcupine: A Synthesizing Compiler for Vectorized Homomorphic Encryption

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 overheads and compilation challenges. Recent work has made significant advances to address the performance overheads but automatic compilation of efficient HE kernels remains relatively unexplored. This paper presents Porcupine, an optimizing compiler, and HE DSL named Quill to automatically generate HE code using program synthesis. HE poses three major compilation challenges: it only supports a limited set of SIMD-like operators, it uses long-vector operands, and decryption can fail if ciphertext noise growth is not managed properly. Quill captures the underlying HE operator behavior that enables Porcupine to reason about the complex trade-offs imposed by the challenges and generate optimized, verified HE kernels. To improve synthesis time, we propose a series of optimizations including a sketch design tailored to HE and instruction restriction to narrow the program search space. We evaluate Procupine using a set of kernels and show speedups of up to 51% (11% geometric mean) compared to heuristic-driven hand-optimized kernels. Analysis of Porcupine's synthesized code reveals that optimal solutions are not always intuitive, underscoring the utility of automated reasoning in this domain.

cs.CR↗

arXiv2019

Synthesizing Number Generators for Stochastic Computing using Mixed Integer Programming

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 implementations require deterministic or pseudo-random number sequences which are optimally correlated to generate bitstreams and achieve accurate results. Unfortunately, the size of the search space makes manually designing optimally correlated number sequences a difficult task. To automate this design burden, we propose a synthesis formulation using mixed integer programming to automatically generate optimally correlated number sequences. In particular, our synthesis formulation improves the accuracy of arithmetic operations such as multiplication and squaring circuits by up to 2.5x and 20x respectively. We also show how our technique can be extended to scale to larger circuits.

cs.ET↗

arXiv2018

Stochastic Synthesis for Stochastic Computing

Stochastic computing (SC) is an emerging computing technique which offers higher computational density, and lower power over binary-encoded (BE) computation. Unlike BE computation, SC encodes values as probabilistic bitstreams which makes designing new circuits unintuitive. Existing techniques for synthesizing SC circuits are limited to specific classes of functions such as polynomial evaluation or constant scaling. In this paper, we propose using stochastic synthesis, which is originally a program synthesis technique, to automate the task of synthesizing new SC circuits. Our results show stochastic synthesis is more general than past techniques and can synthesize manually designed SC circuits as well as new ones such as an approximate square root unit.

cs.ET↗

arXiv2018

Correlation Manipulating Circuits for Stochastic Computing

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 SC arithmetic circuits operate on one or more bitstreams. In many cases, the input bitstreams must be correlated or uncorrelated for SC arithmetic to produce accurate results. As a result, a key challenge for designing SC accelerators is manipulating the impact of correlation across SC operations. This paper presents and evaluates a set of novel correlation manipulating circuits to manage correlation in SC computation: a synchronizer, desynchronizer, and decorrelator. We then use these circuits to propose improved SC maximum, minimum, and saturating adder designs. Compared to existing correlation manipulation techniques, our circuits are more accurate and up to 3x more energy efficient. In the context of an image processing pipeline, these circuits can reduce the total energy consumption by up to 24%.

eess.SP↗

arXiv2017

Application-Driven Near-Data Processing for Similarity Search

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, computer vision, databases, computational biology, and computer graphics. At its core, similarity search manifests as k-nearest neighbors (kNN), a computationally simple primitive consisting of highly parallel distance calculations and a global top-k sort. However, kNN is poorly supported by today's architectures because of its high memory bandwidth requirements. This paper proposes an application-driven near-data processing accelerator for similarity search: the Similarity Search Associative Memory (SSAM). By instantiating compute units close to memory, SSAM benefits from the higher memory bandwidth and density exposed by emerging memory technologies. We evaluate the SSAM design down to layout on top of the Micron hybrid memory cube (HMC), and show that SSAM can achieve up to two orders of magnitude area-normalized throughput and energy efficiency improvement over multicore CPUs; we also show SSAM is faster and more energy efficient than competing GPUs and FPGAs. Finally, we show that SSAM is also useful for other data intensive tasks like kNN index construction, and can be generalized to semantically function as a high capacity content addressable memory.

cs.DC↗

arXiv2017

Similarity Search on Automata Processors

Similarity search is a critical primitive for a wide variety of applications including natural language processing, content-based search, machine learning, computer vision, databases, robotics, and recommendation systems. At its core, similarity search is implemented using the k-nearest neighbors (kNN) algorithm, where computation consists of highly parallel distance calculations and a global top-k sort. In contemporary von-Neumann architectures, kNN is bottlenecked by data movement which limits throughput and latency. In this paper, we present and evaluate a novel automata-based algorithm for kNN on the Micron Automata Processor (AP), which is a non-von Neumann near-data processing architecture. By employing near-data processing, the AP minimizes the data movement bottleneck and is able to achieve better performance. Unlike prior work in the automata processing space, our work combines temporal encodings with automata design to augment the space of applications for the AP. We evaluate our design's performance on the AP and compare to state-of-the-art CPU, GPU, and FPGA implementations; we show that the current generation of AP hardware can achieve over 50x speedup over CPUs while maintaining competitive energy efficiency gains. We also propose several automata optimization techniques and simple architectural extensions that highlight the potential of the AP hardware.

cs.DC↗

arXiv2017

Energy-Efficient Hybrid Stochastic-Binary Neural Networks for Near-Sensor Computing

Recent advances in neural networks (NNs) exhibit unprecedented success at transforming large, unstructured data streams into compact higher-level semantic information for tasks such as handwriting recognition, image classification, and speech recognition. Ideally, systems would employ near-sensor computation to execute these tasks at sensor endpoints to maximize data reduction and minimize data movement. However, near- sensor computing presents its own set of challenges such as operating power constraints, energy budgets, and communication bandwidth capacities. In this paper, we propose a stochastic- binary hybrid design which splits the computation between the stochastic and binary domains for near-sensor NN applications. In addition, our design uses a new stochastic adder and multiplier that are significantly more accurate than existing adders and multipliers. We also show that retraining the binary portion of the NN computation can compensate for precision losses introduced by shorter stochastic bit-streams, allowing faster run times at minimal accuracy losses. Our evaluation shows that our hybrid stochastic-binary design can achieve 9.8x energy efficiency savings, and application-level accuracies within 0.05% compared to conventional all-binary designs.

cs.AR↗