T1C-IR - Intermediate Representation for T1C Neuromorphic Hardware
T1C-IR is an intermediate representation designed for Type 1 Compute (T1C) neuromorphic hardware. It provides a standardized format for representing spiking neural networks with support for serialization, graph manipulation, and hardware-specific optimizations.
T1C-IR serves as an intermediate format between neuromorphic frameworks and hardware platforms, enabling seamless model portability and deployment.
Read more about T1C-IR in our documentation
Installation
uv add t1c-ir@git@github.com:type1compute/t1cir.git
Or with pip:
pip install t1c-ir
Usage
T1C-IR serves as a format between neuromorphic platforms and will be installed alongside your framework of choice. Using T1C-IR follows a simple pattern when you want to move from a source to a target platform:
from t1c.ir import read, write
# Define a model
my_model = ...
# Save the model (source platform)
write("my_graph.t1c", my_model)
# Load the model (target platform)
imported_graph = read("my_graph.t1c")
Quick Start
from t1c.ir import Graph, Input, Output, Affine, LIF, read, write
import numpy as np
# Create a simple network
nodes = {
"input": Input(input_type={"input": np.array([784])}),
"fc1": Affine(
weight=np.random.randn(128, 784).astype(np.float32),
bias=np.zeros(128, np.float32)
),
"lif1": LIF(
tau=np.ones(128, np.float32) * 10.0,
r=np.ones(128, np.float32) * 10.0,
v_leak=np.zeros(128, np.float32),
v_threshold=np.ones(128, np.float32)
),
"output": Output(output_type={"output": np.array([128])})
}
edges = [("input", "fc1"), ("fc1", "lif1"), ("lif1", "output")]
graph = Graph(nodes=nodes, edges=edges)
# Serialize to file
write("model.t1c", graph)
# Load from file
loaded = read("model.t1c")
See our example section for a complete pipeline from snnTorch to T1C hardware.
Frameworks that currently support T1C-IR
| Framework | Write to T1C-IR | Read from T1C-IR | Examples |
|---|---|---|---|
| snnTorch | ✓ | ✓ | snnTorch to T1C tutorial |
Primitives
T1C-IR supports the following computational primitives:
| Primitive | Description |
|---|---|
Input |
Network input node |
Output |
Network output node |
Affine |
Fully-connected layer (Linear) |
SpikingAffine |
Hardware-optimized spiking linear layer |
Conv2d |
2D convolution |
SepConv2d |
Depthwise separable convolution |
MaxPool2d |
2D max pooling |
AvgPool2d |
2D average pooling |
Upsample |
2D spatial upsampling (nearest/bilinear) |
Flatten |
Tensor flattening |
LIF |
Leaky Integrate-and-Fire neuron |
Skip |
Skip/residual connection |
NIR Acknowledgement
T1C-IR is inspired by and builds upon the Neuromorphic Intermediate Representation (NIR) project, a collaborative effort to create a standardized format for neuromorphic computing across multiple platforms.
We acknowledge the NIR authors and their published work:
Pedersen, Jens E. et al. "Neuromorphic intermediate representation: A unified instruction set for interoperable brain-inspired computing." Nature Communications 15, 8122 (2024). https://doi.org/10.1038/s41467-024-52259-9
T1C-IR vs NIR: Key Differences
T1C-IR is not a fork of NIR. It is an independent implementation that shares similar design principles but is specifically tailored for Type 1 Compute (T1C) FPGA hardware. The key differences are:
| Aspect | NIR | T1C-IR |
|---|---|---|
| Target | Cross-platform interoperability | T1C FPGA hardware |
| Primitives | General-purpose set (17+ primitives) | Hardware-focused subset |
| Serialization | HDF5 (.nir files) |
HDF5 (.t1c files) |
| Graph structure | Allows cycles (recurrent) | DAG-only (temporal recurrence via state) |
| File format | Compatible structure | Similar but independent |
Primitives Comparison
| T1C-IR Primitive | NIR Equivalent | Notes |
|---|---|---|
Affine |
Affine |
Same semantics (y = Wx + b) |
LIF |
LIF |
Same NIR-compliant dynamics |
Conv2d |
Conv2d |
Same semantics |
Flatten |
Flatten |
Same semantics |
MaxPool2d |
SumPool2d |
T1C-IR uses max, NIR uses sum |
AvgPool2d |
AvgPool2d |
Same semantics |
Upsample |
(none) | T1C-IR addition: FPN/detection support |
Skip |
(edge-based) | T1C-IR addition: Explicit skip node |
SpikingAffine |
(none) | T1C-IR addition: Hardware hints |
SepConv2d |
(none) | T1C-IR addition: Mobile/edge efficiency |
| (none) | Linear |
NIR has weight-only linear (no bias) |
| (none) | IF, LI, I |
NIR has more neuron variants |
| (none) | CubaLIF, CubaLI |
NIR has current-based neurons |
| (none) | Delay |
NIR has explicit delay nodes |
| (none) | Threshold |
NIR has standalone threshold |
| (none) | Scale |
NIR has scaling operation |
| (none) | Conv1d |
NIR has 1D convolution |
Why T1C-IR is Separate
-
Hardware specificity: T1C-IR includes primitives like
SpikingAffinewith quantization hints (weight_bits,accumulator_bits) that are specific to T1C FPGA compilation. -
DAG enforcement: T1C hardware benefits from directed acyclic graphs for pipelining. Temporal recurrence is handled via stateful neurons across timesteps, not graph cycles.
-
Skip as node: T1C-IR represents skip/residual connections as explicit nodes rather than edge annotations. This provides clearer hardware mapping for T1C compilers.
-
Focused primitive set: T1C-IR only includes primitives relevant to T1C deployment, avoiding complexity from unused neuron types.
-
Independent evolution: T1C-IR can evolve to match T1C hardware requirements without being constrained by cross-platform compatibility.
File Format Compatibility
T1C-IR .t1c files use HDF5 with a structure similar to NIR .nir files:
├── version # Format version string
└── node/ # Graph root
├── type # "Graph"
├── nodes/ # Node dictionary
│ ├── input/
│ ├── fc1/
│ └── ...
└── edges/ # Edge list
├── src # Source node names
└── dst # Destination node names
While structurally similar, T1C-IR files are not directly interchangeable with NIR files due to:
- Different primitive type names in some cases
- T1C-IR-specific primitives (
SpikingAffine,SepConv2d,Skip) - Different version strings
Documentation
For more information, visit the T1C-IR documentation.
1 maintainer
Metadata
1 maintainer
Release files for t1c-ir 0.0.2
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| Download URL | t1c_ir-0.0.2-cp310-cp310-macosx_11_0_arm64.whl |
|---|---|
| Size | 500.5 kB |
| Tags | CPython 3.10 macOS 11.0+ ARM64 |
|
SHA-256 checksum How to use checksums |
0d86f16b8589cfcc04177c83f00b429dcdba31db11e476328746b0130c6c820e
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BLAKE2b-256 checksum How to use checksums |
354187a58a514f41bbf657ac80bd4c1ceff166363bff34d97473c52ada570ceb
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
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Signed by GitHub Actions, verified by PyPI on Feb 4, 2026.
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