As AI increasingly moves beyond cloud environments into robotics, industrial automation, autonomous systems, and smart infrastructure, companies are focusing on building energy-efficient compute platforms capable of supporting real-world AI deployment at scale. Here’s a look at five companies contributing to the infrastructure powering the next generation of Physical AI.
1. SiMa.ai
SiMa.ai is building power-efficient machine learning platforms designed to simplify and accelerate the deployment of Physical AI applications across robotics, industrial automation, autonomous systems, and smart infrastructure. Through its purpose-built MLSoC architecture and integrated Palette software platform, the company enables real-time AI inference at the edge with significantly lower power consumption and reduced dependence on cloud-heavy compute infrastructure. By combining high-performance edge AI with simplified deployment workflows, SiMa.ai is helping enterprises scale AI more efficiently in real-world environments.
2. Arm Holdings
Arm is known for designing energy-efficient processor architectures widely used across smartphones, automotive systems, and edge devices. As AI moves into real-world environments, Arm is helping companies deploy AI workloads with lower power consumption by developing low-energy chip designs optimized for edge AI and intelligent systems.
3. Graphcore
Graphcore develops AI processors specifically built for machine learning workloads. Its Intelligence Processing Units (IPUs) are designed to improve processing efficiency and reduce infrastructure strain by optimizing parallel AI computation, enabling enterprises to run complex AI models more efficiently.
Cerebras Systems is focused on large-scale AI compute systems aimed at improving AI training and inference efficiency. Its wafer-scale processor architecture reduces data movement and simplifies AI compute workflows, helping lower the energy and infrastructure requirements associated with large AI workloads.
5. Hailo
Hailo builds AI accelerators optimized for edge devices and autonomous systems. The company focuses on enabling high-performance AI inference with lower power consumption, allowing applications such as robotics, smart cameras, and industrial systems to process AI workloads locally and more efficiently.
