Inferensys

Glossary

SoftPLC

A SoftPLC is a software-based implementation of a programmable logic controller that executes real-time control logic on a general-purpose industrial PC (IPC) instead of proprietary embedded hardware, enabling virtualization, IT/OT convergence, and direct integration with AI inference runtimes.
Developer testing AI inference on mobile phone in hand, laptop with optimization code visible, casual tech review moment.
SOFTWARE-DEFINED CONTROL

What is a SoftPLC?

A SoftPLC is a software-based implementation of a Programmable Logic Controller that executes on a general-purpose industrial PC, virtualizing traditional proprietary hardware control functions.

A SoftPLC is a software-based implementation of a Programmable Logic Controller that runs on a general-purpose industrial PC (IPC) instead of proprietary, vendor-locked hardware. It executes the same IEC 61131-3 control languages—ladder logic, structured text, and function block diagrams—within a real-time operating system or hypervisor, abstracting the control runtime from the physical backplane.

By decoupling control logic from dedicated silicon, a SoftPLC enables direct integration with modern AI inference engines, OPC UA Pub/Sub data pipelines, and containerized microservices on the same edge node. This convergence collapses the traditional operational technology (OT) and information technology (IT) stack, allowing deterministic machine control and predictive analytics to coexist on a single, consolidated hardware platform.

ARCHITECTURAL FOUNDATIONS

Core Characteristics of SoftPLC Platforms

SoftPLC platforms decouple control logic from proprietary hardware, enabling industrial automation to run on general-purpose compute. The following characteristics define their technical architecture and operational advantages.

01

Hardware Abstraction Layer

A software intermediary that decouples IEC 61131-3 control logic from the underlying physical hardware. This layer translates generic control instructions into hardware-specific calls, enabling the same application to execute across diverse industrial PCs, IPCs, and embedded systems without modification.

  • Eliminates vendor lock-in by separating runtime from silicon
  • Supports real-time Linux kernels and hypervisor-based partitioning
  • Enables seamless migration between x86 and ARM64 architectures
< 100 µs
Typical Jitter on RT Kernel
02

IEC 61131-3 Runtime Engine

A deterministic execution environment that compiles and runs the five standard PLC programming languages: Ladder Diagram, Structured Text, Function Block Diagram, Sequential Function Chart, and Instruction List. The runtime manages task scheduling, I/O scanning, and program execution within strict cycle time constraints.

  • Supports multi-tasking with configurable priority and cycle times
  • Provides online editing without stopping production processes
  • Maintains backward compatibility with legacy PLC codebases
03

Co-Located AI Inference

The ability to execute trained neural network models directly alongside deterministic control logic on the same industrial PC. This eliminates the latency and complexity of separate AI accelerator hardware by leveraging ONNX Runtime or TensorRT within the SoftPLC process space.

  • Enables real-time quality inspection integrated with reject actuator control
  • Supports predictive maintenance models that adjust machine parameters autonomously
  • Uses shared memory for zero-copy data transfer between control and inference
04

Virtualized I/O and Fieldbus Integration

A software-defined I/O abstraction that maps physical sensor and actuator signals to logical tags without hardwired addressing. SoftPLC platforms connect to distributed I/O modules over EtherCAT, PROFINET, and EtherNet/IP using standard network interface cards rather than proprietary backplanes.

  • Supports hot-plugging of I/O modules during runtime
  • Enables software-configurable signal conditioning and filtering
  • Bridges legacy fieldbuses to modern OPC UA Pub/Sub architectures
05

Containerized Deployment and Orchestration

Packaging the entire SoftPLC runtime, including its control application and AI models, as a lightweight container managed by orchestration platforms like K3s or Docker. This enables version-controlled, reproducible deployments across factory fleets with automated failover and scaling.

  • Supports blue-green deployments for zero-downtime control updates
  • Enables shadow mode validation of new logic against live production data
  • Integrates with GitOps workflows for auditable change management
06

Deterministic Real-Time Execution

Guaranteed bounded latency for control loop execution through real-time operating system extensions like PREEMPT_RT or dedicated hypervisor cores. SoftPLC platforms achieve cycle times comparable to hardware PLCs while running on general-purpose compute, essential for motion control and high-speed packaging.

  • Achieves sub-millisecond cycle times with proper kernel tuning
  • Isolates control tasks on dedicated CPU cores via affinity masking
  • Implements watchdog timers for fail-safe state transition on deadline miss
SOFTWARE-DEFINED CONTROL

Frequently Asked Questions About SoftPLC

Clear, technically precise answers to the most common questions about software-based Programmable Logic Controllers, their architecture, and their role in modern industrial automation.

A SoftPLC is a software-based implementation of a Programmable Logic Controller that runs on a general-purpose industrial PC (IPC) or embedded computer rather than proprietary, vendor-specific hardware. It executes the same real-time control logic, typically programmed in IEC 61131-3 languages such as Ladder Diagram, Structured Text, or Function Block Diagram, within a real-time operating system (RTOS) or a real-time kernel extension to a standard OS like Linux. The SoftPLC runtime emulates the deterministic scan cycle of a hardware PLC—reading inputs, executing logic, and writing outputs—within a guaranteed time window. Because it runs on commodity x86 or ARM64 silicon, a SoftPLC can simultaneously host adjacent workloads like inference engines, stream processing, and OPC UA servers on the same physical node, collapsing what would traditionally require multiple separate hardware devices into a single, software-defined automation cell.

Prasad Kumkar

About the author

Prasad Kumkar

CEO & MD, Inference Systems

Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.

His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.