Tag: energy efficiency

  • MIT’s 6-Milliwatt AI Chip Brings Real-Time 3D Mapping to Tiny Robots

    MIT’s 6-Milliwatt AI Chip Brings Real-Time 3D Mapping to Tiny Robots

    Researchers at MIT have unveiled a new system-on-a-chip that could revolutionize how tiny, low-power robots navigate complex environments. The chip, named Gleanmer, enables autonomous drones and other battery-limited devices to construct detailed 3D maps in real time using only about 6 milliwatts of power—roughly the energy of a single LED. This breakthrough, presented at the IEEE Very Large-Scale Integrated Circuits Symposium, promises to make small-scale robotics more capable and energy-efficient than ever.

    A Smarter Approach to 3D Mapping

    Traditional 3D mapping for robots relies on power-hungry systems that process and store thousands of 3D pixels (voxels) per image. The MIT team took a radically different approach by combining an efficient algorithm called GMMap with custom hardware optimized for its workload. Instead of using rigid cube-shaped voxels, GMMap represents obstacles and free space with flexible ellipsoidal blobs known as Gaussians. These shapes can stretch and shrink to match curved objects, storing far less data than traditional methods while still capturing accurate geometry.

    “This paper showcases a key example of how you can leverage co-design of the algorithm and hardware to really push energy efficiency,” says Vivienne Sze, a professor in EECS and senior author of the paper. “Our chip allows you to store very large maps in a very small space, and do it in a very energy efficient manner.”

    Hardware-Algorithm Co-Design at Its Best

    The chip’s efficiency stems from several clever optimizations. First, the GMMap algorithm processes each depth image in a single pass, comparing only neighboring pixels rather than all pixels in an image. This drastically reduces the amount of data that needs to be stored and retrieved. Second, as the robot moves and observes the same object from different angles, overlapping Gaussians are fused into a single compact representation without re-examining the original pixels. Because Gaussians are more compact than raw pixel data, this fusion process runs on-chip with minimal memory traffic.

    The hardware design keeps active Gaussians in small, fast on-chip memory right next to the processing units. “By having a dedicated memory that just stores the objects you’ve seen in the previous few frames, you can access the data much more efficiently,” explains co-lead author Zih-Sing Fu.

    Unmatched Energy Efficiency

    In tests, Gleanmer generated detailed 3D maps in real time while consuming only about 6 milliwatts of power—just 2.5 percent of what the best existing mapping chip requires. When used for path planning, the chip reuses compact Gaussians along the planned route, using only about 20 percent of the energy that conventional approaches would need.

    The low power consumption makes Gleanmer ideal not only for tiny drones inspecting HVAC systems or pipelines, but also for lightweight augmented reality headsets that require continuous environmental mapping for educational medical simulations or repair work. Sertac Karaman, a professor of aeronautics and astronautics and co-author, notes: “Real-time 3D mapping has been the missing piece for small autonomous systems. Gleanmer makes that possible for the first time in a chip you can hold between your fingers.”

    Looking Ahead

    The researchers plan to push energy efficiency even further by moving processing units closer to the sensors that gather environmental data. They are also exploring applications beyond robotics, such as using Gaussians to represent complex schematics for AI reasoning systems. This work was supported by the MIT-MathWorks Fellowship, Amazon, the U.S. National Science Foundation, and Intel.

  • Murakkab: New System Cuts AI Agent Energy Use and Cost by Over 70%

    Murakkab: New System Cuts AI Agent Energy Use and Cost by Over 70%

    Agentic workflows — AI-powered software systems that chain multiple models and tools to complete complex tasks — are becoming the backbone of cloud computing. But their fragmented design often wastes computation, energy, and money. Researchers from MIT and Microsoft have developed a new system called Murakkab that streamlines the design and deployment of these workflows, automatically optimizing them for speed, energy efficiency, and cost.

    With Murakkab, developers describe their application’s goal in plain language, and the system automatically selects the best AI models, tools, hardware configurations, and resource allocations. It adjusts these on the fly based on user priorities, such as minimizing costs or maximizing speed. In tests, Murakkab used only about 35% of the computation, 27% of the energy, and under 25% of the cost compared to traditional methods — without sacrificing performance.

    “Agentic workflows are getting very complicated and quickly becoming the backbone of what cloud providers are doing,” says Gohar Chaudhry, an MIT EECS graduate student and lead author of the paper presented at USENIX OSDI. “Energy usage is a huge concern, so we need to be very careful about how efficient these workflows are.”

    Murakkab also adapts dynamically when new models or hardware become available, eliminating the need for developers to manually reconfigure their systems. The researchers plan to expand the system to more complex workflows and larger computing clusters.

  • MIT Researchers Unveil Ways to Cut Data Center Energy Use and Boost Sustainability

    MIT Researchers Unveil Ways to Cut Data Center Energy Use and Boost Sustainability

    A new study from MIT suggests that flexibility in the timing of electricity consumption at data centers could lower consumer costs. The research highlights how adjusting when energy is used can help manage demand and reduce strain on the grid.

    In related work, MIT researchers have developed a system called Murakkab that improves the speed and energy-efficiency of AI agents. This innovation optimizes the design and deployment of multistep workflows powering AI applications.

    Another project introduces a computer model that enables bridges and buildings to use less material while remaining buildable. The approach bridges the gap between optimized design and real-world construction constraints.

    MIT Professor Susan Solomon was named a 2026 Tang Prize laureate for her groundbreaking work on atmospheric chemistry, which helped lay the foundation for ozone layer recovery and demonstrated the lasting impacts of carbon emissions on climate.

    The MIT Environmental Solutions Journalism Fellowship has expanded climate reporting through local messengers, reaching nearly 3 million readers and listeners with community-centered coverage.

    A startup co-founded by two MIT researchers, Ferveret, uses a nuclear-inspired cooling system to reduce energy and water needed for cooling chips that power AI, making data centers more sustainable.

    Other MIT projects explore low-cost personal cooling and emissions-free air conditioning to address extreme heat, while researchers develop innovative carbon capture methods and a low-cost technique to extract lithium from rocks.

    A study on wetlands preservation shows that tradeoffs between conservation and development can be less stark with a policy featuring tradeable offsets and taxes. The MIT Asia Real Estate Initiative expands into booming Asian cities, and MIT Sloan fellows share insights on leading a sustainable future.

    For most U.S. drivers, electric vehicles offer emissions benefits and cost savings, with individual driving patterns and regional electricity mix playing key roles.