An Intermediate Representation (IR) is a data structure or code format used internally by a compiler or framework to represent a program during transformation and optimization. In the context of machine learning, IRs serve as the bridge between high-level model definitions (like those written in PyTorch or TensorFlow) and the low-level machine code that actually runs on hardware.
Think of an IR as a universal translator. When you write a neural network in PyTorch, you're using Python—a high-level language that GPUs and specialized accelerators don't understand directly. The IR captures the essential structure and operations of your model in a hardware-agnostic format, allowing compilers to optimize and eventually generate code for specific targets like NVIDIA GPUs, Google TPUs, or neuromorphic chips.
Why IRs Matter for ML Deployment
The proliferation of hardware accelerators has made IRs essential. A decade ago, most ML workloads ran on CPUs or NVIDIA GPUs with CUDA. Today, the landscape includes:
- GPUs from NVIDIA, AMD, and Intel
- TPUs from Google, designed specifically for tensor operations
- NPUs (Neural Processing Units) in mobile devices from Apple, Qualcomm, and others
- FPGAs for customizable, low-latency inference
- Neuromorphic chips like Intel's Loihi or IBM's TrueNorth for brain-inspired computing
Without a common IR, framework developers would need to write separate backends for every hardware target—an exponentially growing maintenance burden. IRs decouple the frontend (model definition) from the backend (hardware-specific code generation), enabling a many-to-many mapping through a single intermediate layer.
Popular IRs in the ML Ecosystem
ONNX (Open Neural Network Exchange)
ONNX is perhaps the most widely adopted IR for model interchange. Developed by Microsoft and Facebook (now Meta), it defines a common set of operators and a standard file format. You can export a model from PyTorch, convert it to ONNX, and then run it using ONNX Runtime on various hardware backends. However, ONNX is primarily designed for inference—it captures the forward pass but not training-specific constructs.
TVM's Relay IR
Apache TVM is an open-source compiler stack for deep learning. Its high-level IR, called Relay, represents neural networks as functional programs with support for control flow, recursion, and automatic differentiation. Relay enables sophisticated optimizations like operator fusion, layout transformation, and quantization before lowering to TVM's low-level tensor IR for final code generation.
MLIR (Multi-Level IR)
MLIR, developed by Google and now part of the LLVM project, isn't a single IR but a framework for building IRs. It introduces the concept of "dialects"—domain-specific IR fragments that can be mixed and matched. For ML, there are dialects for TensorFlow operations, linear algebra, GPU kernels, and more. MLIR's power lies in its ability to represent computation at multiple abstraction levels within a single infrastructure, enabling progressive lowering from high-level graphs to machine code.
XLA (Accelerated Linear Algebra)
XLA is Google's domain-specific compiler for linear algebra. It's the default compiler for JAX and can be used with TensorFlow. XLA's HLO (High-Level Optimizer) IR represents computations as dataflow graphs of tensor operations, enabling whole-program optimization including fusion, memory scheduling, and target-specific code generation for TPUs and GPUs.
The Anatomy of an ML IR
Most ML IRs share common characteristics:
- Graph-based representation: Operations are nodes, and data dependencies are edges. This makes dataflow analysis and transformation straightforward.
- Typed tensors: Every value has a known shape and data type, enabling shape inference and memory planning.
- Static single assignment (SSA): Each variable is assigned exactly once, simplifying optimization passes.
- Operator semantics: A defined set of primitive operations (convolution, matrix multiply, activation functions) with precise mathematical semantics.
For example, a simple y = relu(matmul(W, x) + b) might be represented in an IR as:
%0 = matmul(%W, %x) : tensor<128x64xf32>
%1 = add(%0, %b) : tensor<128xf32>
%2 = relu(%1) : tensor<128xf32>
return %2IRs for Neuromorphic Computing
Standard ML IRs assume synchronous, clock-driven execution with dense tensor operations. Neuromorphic hardware operates differently—it's event-driven, asynchronous, and processes sparse spike trains rather than dense activations. This fundamental mismatch means that ONNX, Relay, and XLA aren't suitable for neuromorphic deployment without significant modification.
Neuromorphic IRs must capture:
- Temporal dynamics: Spike timing, membrane potentials, and refractory periods
- Event-based computation: Operations triggered by spikes, not clock cycles
- Sparse connectivity: Neuron-to-neuron connections that don't fit neatly into tensor abstractions
- Hardware constraints: Neuron counts, synapse limits, and routing topology specific to each chip
This is exactly the problem space I'm working in—building IRs that can faithfully represent spiking neural networks while enabling the compiler optimizations needed for efficient deployment on neuromorphic hardware.
Conclusion
Intermediate representations are the unsung heroes of the ML deployment stack. They enable the portability, optimization, and hardware abstraction that make modern deep learning practical. As hardware diversity continues to grow—especially with the rise of neuromorphic and other non-von Neumann architectures—the importance of well-designed IRs will only increase. Understanding IRs isn't just academic; it's essential for anyone building systems that need to run efficiently on specialized hardware.