AI is moving beyond the data center and into the physical world. Vehicles, robots, industrial systems, sensors, and critical infrastructure increasingly rely on AI to perceive, decide, and act in real time. Physical AI refers to intelligent systems that sense, think, act, and communicate autonomously in platforms outside the data center, where software decisions have real-world consequences and must meet strict requirements for timing, safety, and reliability.

Unlike cloud workloads that can tolerate latency and variability, Physical AI systems must respond predictably. A vehicle executing an emergency maneuver, a robotic arm adapting to changing conditions, or an industrial controller responding to a fault condition must process information and act within strict timing constraints. These workloads require predictable latency, low power consumption, functional safety, and close coordination between software and silicon.

AI accelerators can identify objects, generate predictions, and process sensor data. Physical AI systems must also coordinate motion, manage safety functions, respond to changing conditions, and guarantee real-time behavior. Those requirements place equal importance on deterministic control and system orchestration. 

The MIPS many engineers remember as an embedded CPU company has evolved into the leading provider of RISC-V-based compute solutions, virtual platforms, and software-to-silicon development capabilities designed for Physical AI systems. This article explains what defines MIPS today and the engineering teams that benefit most from its technology.

From Embedded CPU IP Vendor to an AI-Native Platform Company

Since 2021, MIPS has embraced RISC-V as the architectural foundation for its next generation of compute solutions. RISC-V is an open instruction set architecture that allows companies to build differentiated processor implementations while benefiting from a shared ecosystem of software, tools, and operating system support.

MIPS develops commercial processor IP, software tools, physical AI platforms, and custom application-specific standard products (ASSP) built on that foundation. MIPS uniquely combines compute architectures, virtual platforms, workload optimization, custom chip design, and manufacturing expertise into a unified software-to-silicon workflow.

The company's software-to-silicon workflow consists of four stages:

1. Understand and optimize workloads in a virtual platform.

2. Configure compute subsystems around workload requirements.

3. Optimize SoC architecture for performance, power, safety, and cost targets.

4. Manufacture and scale production using proven semiconductor processes.

This workflow reflects a broader industry shift toward software-defined silicon development, where software requirements increasingly drive architectural decisions much earlier in the design process. An automotive developer can evaluate sensor-processing software in a virtual platform before silicon exists, identify bottlenecks, optimize the compute subsystem, and validate system behavior long before tape-out.

Why RISC-V is the Right Foundation

RISC-V changes how companies access and customize processor architectures by separating the instruction set architecture from the processor IP built on top of it. This approach gives designers greater flexibility to optimize hardware around specific workloads while benefiting from a rapidly expanding ecosystem.

There are four engineering arguments that matter: 

• Modularity: RISC-V is designed as a small base instruction set with optional extensions. Designers start with the base and add only what the workload requires: AI extensions, security, DSP, or vector capabilities, instead of paying for and carrying instructions they will never execute.

• Cost and Supply-Chain Independence: No upfront licensing fees, no per-chip royalties, and reduced exposure to single-vendor risk. For products shipping at high volume, the unit economics matters; whereas for products shipping into geopolitically sensitive markets, the supply-chain independence matters even more.

• Architectural Flexibility: RISC-V allows workload-specific optimization and tighter hardware/software co-design than closed architectures permit. Teams can shape the ISA around the application, rather than the other way around.

• Ecosystem Momentum: Multiple contributing companies, maturing toolchains, growing OS support, and an expanding developer base are turning what was a research architecture five years ago into a mainstream design choice today.

The markets where these advantages are most valuable align closely with MIPS' strategic focus areas: automotive, aerospace and defense, robotics, industrial automation, intelligent infrastructure, IoT, and AI-enabled edge systems. These applications increasingly require workload-specific optimization, functional safety, and long product lifecycles that benefit from the flexibility of RISC-V.

Why OEMs Are Seeking Greater Control Over Semiconductor Design

The deeper force behind RISC-V adoption is that AI workloads are evolving faster than traditional silicon development cycles. By the time a fixed-architecture SoC ships, the workloads it was optimized for have often moved on. That mismatch has pushed OEMs to seek more control over workload-specific optimization, custom instruction extensions, hardware/software co-design, and long-term platform differentiation.

Traditional fixed architectures can limit all four. They constrain how deeply teams can tune the silicon around proprietary workloads, and they tie product roadmaps to someone else's release schedule. RISC-V removes that ceiling. Companies can customize the architecture around their own workloads and reduce dependency on closed ecosystems they do not control.

This broader industry shift toward software-first platform development, where the software stack, not the historical accumulation of architectural decisions, drives what the silicon needs to look like. Virtual platforms unlock these insights at the deepest levels by bringing software development into the architecture design stage of development. 

Engineered for Physical AI and Real-Time Workloads

MIPS focuses on Physical AI: intelligent systems that sense, understand, and act within the physical world. These systems include autonomous vehicles, industrial robots, aerospace platforms, intelligent machines, and edge infrastructure where software decisions have direct real-world consequences.

This distinction separates Physical AI from cloud-centric AI. AI accelerators deliver throughput for perception and inference workloads, while deterministic compute coordinates system behavior, manages safety functions, and ensures that actions occur within predictable timing boundaries. Real-world systems require both capabilities.

Many use cases follow from that:

• Automotive: sensor fusion, domain controllers, safety monitoring in zonal controllers, ADAS, and autonomous driving.

• Aerospace and Defense: platforms that need functional safety, deterministic processing, and open stacks for AI inference 

• Industrial Automation and PLCs: for motion control, safety domains, and real-time event-driven applications

• Autonomous Embedded Devices and Intelligent Sensors.

• Robotics and Intelligent Edge Infrastructure.

The engineering priorities across all of these are the same: low latency, predictable performance, and functional safety.

The Atlas Explorer Virtual Platform 

The software-to-silicon flow requires tools that make the “software” component tangible and actionable, not aspirational. MIPS introduced Atlas Explorer, the software development and optimization platform for its Atlas portfolio of compute subsystems. 

Atlas Explorer enables engineering teams to: 

• Implement shift-left optimization for software and platform, where tuning happens before silicon exists, not after.

• Perform pre-silicon system-level testing of MIPS compute subsystems.

• Create digital-twin modeling of the target platform, so software and silicon teams work from the same reference.

• Gain data-driven insight into how workloads actually execute, not just how they were expected to.

• Share reporting across software, hardware, and systems teams through a common view.

By prioritizing a software-first methodology for silicon engineering, Atlas Explorer helps transform the concept of software-defined hardware into a practical development workflow. 

Built for Modern, Heterogeneous SoCs

Modern chips aren't built around a single general-purpose core anymore. They're built from workload-specific compute blocks: AI accelerators, DSPs, GPUs, and security engines stitched together on a single die.

In increasingly heterogeneous systems, designers often deploy multiple classes of processors. MIPS and ARC provide complementary technologies optimized for different compute requirements, allowing architects to select the right engine for each workload. The role MIPS plays inside a heterogeneous SoC can vary from the control plane and real-time decision engine to embedded safety-capable applications processors and AI inference engines. It serves as the orchestration layer that coordinates the rest of the system and handles the safety-critical, time-sensitive decisions that throughput-oriented accelerators are not designed for.

That role becomes more important, not less, as Physical AI systems mature. The more compute domains a system contains, such as vision acceleration, language models, sensor fusion, and motor control, the more it needs a deterministic core in the middle, making sure they all act together in real time.

In increasingly heterogeneous SoCs, designers require multiple classes of compute optimized for different workloads. Together, MIPS and ARC provide complementary processor technologies that allow architects to deploy the right compute engine for each function, from real-time control and safety management to embedded intelligence and workload-specific processing.

MIPS

Who Needs MIPS?

The three audiences MIPS is targeting today are OEMs, integrators, and IDMs. The needs are different for each market, but the underlying requirement is the same: deterministic, safety-aware compute that can be tuned to the workload. 

OEMs Building Safety-Critical Systems

Automotive is one of the clearest examples of this trend. Software-defined vehicles, zonal architectures, ADAS platforms, automated driving systems, and vehicle control domains all require deterministic compute that meets stringent functional safety requirements. MIPS targets workloads where predictable latency, ASIL-oriented design, long lifecycle support, and real-time decision making are more important than raw application processor performance. The role of MIPS technology is to serve as the real-time compute layer that coordinates and controls safety-critical vehicle functions.

Integrators Building Physical AI Systems

Robotics companies, autonomous-system developers, industrial OEMs, and smart-infrastructure builders all share a common engineering need: deterministic response times, local decision-making without cloud dependence, and reliability over raw throughput. MIPS fits as the real-time compute layer alongside AI accelerators, and as the control core in PLCs, factory automation, and edge infrastructure where uptime and predictability matter more than peak FLOPS. 

IDMs Designing SoCs

The third audience is the companies designing their own silicon: hyperscalers, system companies, and silicon startups building chips in-house, increasingly on RISC-V for the flexibility reasons described above. 

MIPS fits inside their designs as:

• The embedded "brain" for specialized chips (power ICs, RF, sensors, memory controllers) running firmware, managing power states, and handling communication protocols.

• Area- and power-efficient cores for always-on subsystems.

• A flexible licensing and royalty model that matters when products ship in high volume.

• A platform suited to custom instruction extensions and tight hardware/software co-design.

There is also an ecosystem continuity advantage. Some IDMs already have product lines built around MIPS, with existing software, toolchains, and team expertise. The path forward on RISC-V preserves that institutional investment rather than discarding it.

The Bigger Picture and What Comes Next

MIPS today is a RISC-V-based compute company focused on enabling Physical AI systems through software-to-silicon development. Its strategy combines compute IP, virtual platforms, software optimization, and manufacturing expertise to help customers build intelligent systems that operate safely, efficiently, and predictably in the physical world.

This allows engineering teams to evaluate, optimize, and scale systems using a unified development approach. This alignment supports a growing industry shift toward software-defined silicon, where software and hardware are increasingly developed and optimized together rather than through traditional sequential handoffs.

This article is the first in a series. Future pieces will examine RISC-V in next-generation SoCs, the Atlas Explorer platform, MIPS in automotive and Physical AI, how the new combined MIPS and ARC team will expand heterogeneous compute and embedded intelligence, and the patterns engineers should plan for as AI moves to the edge.