TALON IR - Neuromorphic Intermediate Representation
TALON IR is a neuromorphic intermediate representation designed for TALON (Tactical AI at Low-power On-device Nodes) hardware. It provides a standardized format for representing spiking neural networks with support for serialization, graph manipulation, and hardware-specific optimizations.
TALON IR serves as an intermediate format between neuromorphic frameworks and hardware platforms, enabling seamless model portability and deployment.
Read more about TALON IR in our documentation
Installation
uv add talon-ir@git+ssh://git@github.com:type1compute/talonir.git
Or with pip:
pip install talon-ir
Usage
TALON IR serves as a format between neuromorphic platforms and will be installed alongside your framework of choice. Using TALON IR follows a simple pattern when you want to move from a source to a target platform:
from talon.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 talon.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 TALON hardware.
Frameworks that currently support TALON IR
| Framework | Write to TALON IR | Read from TALON IR | Examples |
|---|---|---|---|
| snnTorch | ✓ | ✓ | snnTorch to TALON tutorial |
Primitives
TALON 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
TALON 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
TALON IR vs NIR: Key Differences
TALON IR is not a fork of NIR. It is an independent implementation that shares similar design principles but is specifically tailored for TALON FPGA hardware. The key differences are:
| Aspect | NIR | TALON IR |
|---|---|---|
| Target | Cross-platform interoperability | TALON 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
| TALON 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 |
TALON IR uses max, NIR uses sum |
AvgPool2d |
AvgPool2d |
Same semantics |
Upsample |
(none) | TALON IR addition: FPN/detection support |
Skip |
(edge-based) | TALON IR addition: Explicit skip node |
SpikingAffine |
(none) | TALON IR addition: Hardware hints |
SepConv2d |
(none) | TALON 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 TALON IR is Separate
-
Hardware specificity: TALON IR includes primitives like
SpikingAffinewith quantization hints (weight_bits,accumulator_bits) that are specific to TALON FPGA compilation. -
DAG enforcement: TALON hardware benefits from directed acyclic graphs for pipelining. Temporal recurrence is handled via stateful neurons across timesteps, not graph cycles.
-
Skip as node: TALON IR represents skip/residual connections as explicit nodes rather than edge annotations. This provides clearer hardware mapping for TALON compilers.
-
Focused primitive set: TALON IR only includes primitives relevant to TALON deployment, avoiding complexity from unused neuron types.
-
Independent evolution: TALON IR can evolve to match TALON hardware requirements without being constrained by cross-platform compatibility.
File Format Compatibility
TALON 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, TALON IR files are not directly interchangeable with NIR files due to:
- Different primitive type names in some cases
- TALON IR-specific primitives (
SpikingAffine,SepConv2d,Skip) - Different version strings
Documentation
For more information, visit the TALON IR documentation.
1 maintainer
Metadata
1 maintainer
Release files for talon-ir 0.0.2
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| Size | 688.4 kB |
| Tags | CPython 3.10 macOS 11.0+ ARM64 |
|
SHA-256 checksum How to use checksums |
b1b36b3cf8b7f86225a277910dd5c8cd642ce650ec3aae94064033c2fc371a2f
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BLAKE2b-256 checksum How to use checksums |
09dc3dd61cc7039bc03e5dc8273c2ab5a2a7ddfc55aeadc3fed80eeffbc48bd7
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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/3.13.12
|
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