> ## Documentation Index
> Fetch the complete documentation index at: https://docs.thrindex.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Hardware Targets

> How to compile and deploy THRINDEX models to neuromorphic hardware. Currently supported: BrainChip AKD1500.

## Overview

THRINDEX separates compilation from execution. A model is compiled once to a `.thx` artifact that carries the target it was built for. On a machine with the appropriate hardware and driver, the artifact is loaded and run by the corresponding backend.

```
Developer machine           Device (e.g. Raspberry Pi + AKD1500)
──────────────────          ───────────────────────────────────────
thx.compile(model,          akida-runtime model.fbz
  "model.thx",                  ← reads JSON batch from stdin
  target="akida-akd1500")       → writes JSON output to stdout
        ↓
akida_compile.py
  model.thx → model.fbz
```

***

## BrainChip AKD1500

The `akida-akd1500` target maps THRINDEX models to BrainChip's [Akida 1.0](https://doc.brainchipinc.com/) neuromorphic processor.

### Capability

| Field                | Value                                    |
| -------------------- | ---------------------------------------- |
| Target name          | `akida-akd1500`                          |
| Native timestep      | 1.0 ms                                   |
| Native delay support | None (`native_delay_max_steps = 0`)      |
| Delay fallback       | Reject                                   |
| Precision            | 4-bit integer per tensor                 |
| Weight range         | `[-7, 7]` (int8 container, 4-bit values) |

### What the AKD1500 supports

AKD1500 executes feedforward integer networks. Supported layer types:

* **Dense** (`snn.Dense`) — fully connected, no delays
* **Conv2d** (`snn.Conv2d`) — 2D convolution, no delays

### What the AKD1500 rejects

| Condition                                   | Error                  |
| ------------------------------------------- | ---------------------- |
| LIF layer in artifact                       | [E0401](/errors/E0401) |
| Synaptic delays in artifact                 | [E0402](/errors/E0402) |
| Non-finite (NaN / Inf) weights              | [E0403](/errors/E0403) |
| Artifact compiled for a different target    | [E0404](/errors/E0404) |
| Input with more than one timestep (`T > 1`) | [E0407](/errors/E0407) |

AKD1500 is not an SNN backend. It does not implement leaky integrate-and-fire dynamics, temporal state, or spike-triggered reset. If your model uses LIF neurons, use `target="sim"` instead. See [E0401](/errors/E0401) for a full explanation.

### Weight quantization

Weights are quantized from `f32` to 4-bit signed integers during the Tier-1 compile step:

```
scale = max(|W|) / 7
W_int = round(W / scale)  — clamped to [-7, 7]
```

The quantization error is at most `scale / 2` per weight. For models with large weight ranges, this error can be significant. Training with weight regularization that keeps `max(|W|)` small reduces quantization error.

***

## Deployment: two-tier setup

AKD1500 deployment uses two separate machines:

**Tier 1 — Developer machine** (Python + MetaTF)

Requires: `thrindex` + `akida` Python package from BrainChip.

```python theme={null}
import thrindex as thx

# Compile to .thx — validates the model for akida-akd1500
thx.compile(model, "model.thx", target="akida-akd1500")
```

Then convert to the BrainChip `.fbz` runtime format:

```bash theme={null}
python akida_compile.py model.thx model.fbz
```

`akida_compile.py` is included in `crates/thrindex-backends/akida/python/` in the THRINDEX repository.

**Tier 2 — Device** (Raspberry Pi + AKD1500 via M.2)

Requires: THRINDEX built with `--features hardware` and the BrainChip Engine Library.

```bash theme={null}
# Set path to Engine Library source (obtained from BrainChip)
export THRINDEX_AKIDA_ENGINE_PATH=/path/to/engine/engine

# Build the runtime binary
cargo build -p thrindex-backend-akida --features hardware --release

# Run inference
echo '{"batch": [[[0.5, 0.2, 0.8, ...]]]}' | \
  akida-runtime model.fbz --pcie-addr 0001:01:00.0
```

The runtime reads a JSON batch on stdin and writes a JSON result on stdout:

```json theme={null}
// stdin — shape [N_samples, T=1, n_features]
{"batch": [[[0.5, 0.2, 0.8, 0.1, ...]]]}

// stdout on success — shape [N_samples, n_outputs]
{"outputs": [[0.03, 0.91, 0.02, 0.04]]}

// stdout on error
{"error": "E0407: ..."}
```

### Environment variables

| Variable                     | Required      | Description                                           |
| ---------------------------- | ------------- | ----------------------------------------------------- |
| `THRINDEX_AKIDA_ENGINE_PATH` | At build time | Path to the BrainChip Engine Library source drop      |
| `THRINDEX_AKIDA_DEVICE`      | At runtime    | PCIe address of the AKD1500 (default: `0001:01:00.0`) |

### Getting the Engine Library

The Engine Library is proprietary BrainChip software distributed to registered AKD1500 customers. It is not included in the THRINDEX repository. Contact [BrainChip](https://brainchipinc.com/) to obtain it.

***

## Conformance

`akida-akd1500` is **permanently excluded** from the THRINDEX Certified \[v0] conformance suite. The Certified badge measures spike-raster equivalence between a hardware backend and the reference simulator. AKD1500 does not implement SNN dynamics — it performs integer feedforward inference — and cannot produce a spike raster that is equivalent to the LIF simulator output.

When the conformance harness runs, it prints:

```
EXCLUDED akida-akd1500: reason=non-snn-backend; metric=top-1-accuracy-frozen-brainchip-dataset
```

The alternative metric (top-1 accuracy on the frozen BrainChip dataset) is the appropriate correctness measure for AKD1500 inference. See [Conformance Overview](/conformance/overview) for details.