Factor graph is a graph representing the factorization of a probability distribution function and serves as a perfect abstraction in many autonomous machine computing stacks, such as planning, localization, tracking a...
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ISBN:
(纸本)9798350323481
Factor graph is a graph representing the factorization of a probability distribution function and serves as a perfect abstraction in many autonomous machine computing stacks, such as planning, localization, tracking and control, which are challenging tasks for autonomous systems with real-time and energy constraints. In this paper, we present BLITZCRANK, an accelerator for motion planning algorithms using the abstraction of a factor graph. By formulating motion planning as a factor graph inference, we successfully reduce the scale of the problem and utilize the inherent matrix sparsity. BLITZCRANK is able to realize the user-defined optimal design by finding the optimal order of the factor graph inference. With a domain specific balancing order, BLITZCRANK achieves up to 7.4x speed up and 29.7x energy reduction compared to the software implementation on Intel CPU.
Factor graph is a graph representing the factorization of a probability distribution function, and has been utilized in many autonomous machine computing tasks, such as localization, tracking, planning and control etc...
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ISBN:
(纸本)9781450392174
Factor graph is a graph representing the factorization of a probability distribution function, and has been utilized in many autonomous machine computing tasks, such as localization, tracking, planning and control etc. We are developing an architecture with the goal of using factor graph as a common abstraction for most, if not, all autonomous machine computing tasks. If successful, the architecture would provide a very simple interface of mapping autonomousmachine functions to the underlying compute hardware. As a first step of such an attempt, this paper presents our most recent work of developing a factor graph accelerator for LiDAR-Inertial Odometry (LIO), an essential task in many autonomousmachines, such as autonomous vehicles and mobile robots. By modeling LIO as a factor graph, the proposed accelerator not only supports multi-sensor fusion such as LiDAR, inertial measurement unit (IMU), GPS, etc., but solves the global optimization problem of robot navigation in batch or incremental modes. Our evaluation demonstrates that the proposed design significantly improves the real-time performance and energy efficiency of autonomousmachine navigation systems. The initial success suggests the potential of generalizing the factor graph architecture as a common abstraction for autonomous machine computing, including tracking, planning, and control etc.
One key technical challenge in the age of autonomousmachines is the programming of autonomousmachines, which demands the synergy across multiple domains, including fundamental computer science, computer architecture...
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ISBN:
(数字)9781665472982
ISBN:
(纸本)9781665472982
One key technical challenge in the age of autonomousmachines is the programming of autonomousmachines, which demands the synergy across multiple domains, including fundamental computer science, computer architecture, and robotics, and requires expertise from both academia and industry. This paper discusses the programming theory and practices tied to producing real-life autonomousmachines, and covers aspects from high-level concepts down to low-level code generation in the context of specific functional requirements, performance expectation, and implementation constraints of autonomousmachines.
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