233 citations · 833 across the 22 of their papers we have counts for
24 papers
OMU: A Probabilistic 3D Occupancy Mapping Accelerator for Real-time OctoMap at the Edge
Tianyu Jia, En-Yu Yang, Yu-Shun Hsiao +4
Autonomous machines (e.g., vehicles, mobile robots, drones) require sophisticated 3D mapping to perceive the dynamic environment. However, maintaining a real-time 3D map is expensi…
Trireme: Exploring Hierarchical Multi-Level Parallelism for Domain Specific Hardware Acceleration
Georgios Zacharopoulos, Adel Ejjeh, Ying Jing +9
The design of heterogeneous systems that include domain specific accelerators is a challenging and time-consuming process. While taking into account area constraints, designers mus…
NVMExplorer: A Framework for Cross-Stack Comparisons of Embedded Non-Volatile Memories
Lillian Pentecost, Alexander Hankin, Marco Donato +3
Repeated off-chip memory accesses to DRAM drive up operating power for data-intensive applications, and SRAM technology scaling and leakage power limits the efficiency of embedded…
Application-driven Design Exploration for Dense Ferroelectric Embedded Non-volatile Memories
Mohammad Mehdi Sharifi, Lillian Pentecost, Ramin Rajaei +8
The memory wall bottleneck is a key challenge across many data-intensive applications. Multi-level FeFET-based embedded non-volatile memories are a promising solution for denser an…
Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix
Maximilian Lam, Gu-Yeon Wei, David Brooks +2
We show that aggregated model updates in federated learning may be insecure. An untrusted central server may disaggregate user updates from sums of updates across participants give…
Logic Compatible High-Performance Ferroelectric Transistor Memory
Sourav Dutta, Huacheng Ye, Abhishek Khanna +11
Silicon ferroelectric field-effect transistors (FeFETs) with low-k interfacial layer (IL) between ferroelectric gate stack and silicon channel suffers from high write voltage, limi…
RecPipe: Co-designing Models and Hardware to Jointly Optimize Recommendation Quality and Performance
Udit Gupta, Samuel Hsia, Jeff Zhang +6
Deep learning recommendation systems must provide high quality, personalized content under strict tail-latency targets and high system loads. This paper presents RecPipe, a system…
MAVFI: An End-to-End Fault Analysis Framework with Anomaly Detection and Recovery for Micro Aerial Vehicles
Yu-Shun Hsiao, Zishen Wan, Tianyu Jia +6
Safety and resilience are critical for autonomous unmanned aerial vehicles (UAVs). We introduce MAVFI, the micro aerial vehicles (MAVs) resilience analysis methodology to assess th…
AutoPilot: Automating SoC Design Space Exploration for SWaP Constrained Autonomous UAVs
Srivatsan Krishnan, Zishen Wan, Kshitij Bhardwaj +6
Building domain-specific accelerators for autonomous unmanned aerial vehicles (UAVs) is challenging due to a lack of systematic methodology for designing onboard compute. Balancing…
RecSSD: Near Data Processing for Solid State Drive Based Recommendation Inference
Mark Wilkening, Udit Gupta, Samuel Hsia +4
Neural personalized recommendation models are used across a wide variety of datacenter applications including search, social media, and entertainment. State-of-the-art models compr…
Chasing Carbon: The Elusive Environmental Footprint of Computing
Udit Gupta, Young Geun Kim, Sylvia Lee +5
Given recent algorithm, software, and hardware innovation, computing has enabled a plethora of new applications. As computing becomes increasingly ubiquitous, however, so does its…
Cross-Stack Workload Characterization of Deep Recommendation Systems
Samuel Hsia, Udit Gupta, Mark Wilkening +3
Deep learning based recommendation systems form the backbone of most personalized cloud services. Though the computer architecture community has recently started to take notice of…
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…
CHIPKIT: An agile, reusable open-source framework for rapid test chip development
Paul Whatmough, Marco Donato, Glenn Ko +3
The current trend for domain-specific architectures (DSAs) has led to renewed interest in research test chips to demonstrate new specialized hardware. Tape-outs also offer huge ped…
SMAUG: End-to-End Full-Stack Simulation Infrastructure for Deep Learning Workloads
Sam Likun Xi, Yuan Yao, Kshitij Bhardwaj +3
In recent years, there has been tremendous advances in hardware acceleration of deep neural networks. However, most of the research has focused on optimizing accelerator microarchi…
A binary-activation, multi-level weight RNN and training algorithm for ADC-/DAC-free and noise-resilient processing-in-memory inference with eNVM
Siming Ma, David Brooks, Gu-Yeon Wei
We propose a new algorithm for training neural networks with binary activations and multi-level weights, which enables efficient processing-in-memory circuits with embedded nonvola…
MLPerf Training Benchmark
Peter Mattson, Christine Cheng, Cody Coleman +34
Machine learning (ML) needs industry-standard performance benchmarks to support design and competitive evaluation of the many emerging software and hardware solutions for ML. But M…
AdaptivFloat: A Floating-point based Data Type for Resilient Deep Learning Inference
Thierry Tambe, En-Yu Yang, Zishen Wan +5
Conventional hardware-friendly quantization methods, such as fixed-point or integer, tend to perform poorly at very low word sizes as their shrinking dynamic ranges cannot adequate…
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…
Benchmarking TPU, GPU, and CPU Platforms for Deep Learning
Yu Emma Wang, Gu-Yeon Wei, David Brooks
Training deep learning models is compute-intensive and there is an industry-wide trend towards hardware specialization to improve performance. To systematically benchmark deep lear…
Cloud No Longer a Silver Bullet, Edge to the Rescue
Yuhao Zhu, Gu-Yeon Wei, David Brooks
This paper takes the position that, while cognitive computing today relies heavily on the cloud, we will soon see a paradigm shift where cognitive computing primarily happens on ne…
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…