Interview with Lattice Semiconductor’s Karl Wachswender
As robots become more intelligent, autonomous and connected, much of the attention naturally falls on increasingly powerful AI models. But underneath those models is an increasingly complex collection of sensors, processors, control systems and security hardware that must operate reliably – often within strict limits…
As robots become more intelligent, autonomous and connected, much of the attention naturally falls on increasingly powerful AI models. But underneath those models is an increasingly complex collection of sensors, processors, control systems and security hardware that must operate reliably – often within strict.
Lattice Semiconductor is one of the companies developing technology for this less visible layer of the robotics stack. The flexibility of FPGAs is becoming particularly relevant as robotics moves toward greater edge processing.
What Happened
Lattice is targeting applications including industrial robots, autonomous mobile robots and other physical AI systems, where deterministic real-time control, low latency and power efficiency can be as important as raw computing performance. In this interview, Robotics & Automation News speaks with Karl.
Founded in 1983 and headquartered in Oregon, the company specializes in low-power field programmable gate arrays (FPGAs), which can be programmed and reprogrammed for functions ranging from machine vision and sensor fusion to motor.
How important are programmable hardware architectures in extending the operational life of industrial and service robots?
R&AN: Robotics manufacturers increasingly want platforms that can evolve after deployment rather than becoming obsolete after a few years.
Key Details
Wachswender discusses why more perception, sensor fusion, object tracking and motor-control workloads are moving onto robots rather than relying on cloud processing, and how programmable architectures can allow machines to evolve after deployment instead of becoming obsolete as requirements change. He also.
This single-source option helps further address depth-perception and self-occlusion challenges that hinder real-world interaction, taking a meaningful step forward for execution.
By combining a compact camera module with a dynamic FPGA, developers were able to make single-sensor 3D vision at the edge a reality.
To better understand the impact of these advances, take a look at this recent collaboration between Lattice and AIRY3D.
Why It Matters
The discussion also looks ahead to the semiconductor opportunities created by humanoids, industrial automation, autonomous mobile robots and healthcare robotics as intelligent machines become more widely deployed. Robotics & Automation News: Humanoid robots are becoming increasingly capable, but they also have strict.
Offloading these kinds of depth processing, sensor fusion, object tracking, and region-of-interest detection workloads from centralized systems reduces latency and power costs, in turn making autonomous robots more accessible and reliable.
KW: Machine vision has advanced greatly over the past few years, with FPGA-enabled, edge-based processing helping to filter and preprocess camera sensor data before it reaches central processors.
What Reports Say
Coverage of the story so far points to:
Continued reporting by Robotics & Automation News as more details emerge
