171 citations · 272 across the 11 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…