Retire Quartus backend

.pre-commit-config.yaml

@@ -1,4 +1,4 @@
-exclude: (^hls4ml\/templates\/(vivado|quartus)\/(ap_types|ac_types)\/|^test/pytest/test_report/)
+exclude: (^hls4ml\/templates\/vivado\/ap_types\/|^test/pytest/test_report/)
 
 repos:
 - repo: https://github.com/astral-sh/ruff-pre-commit

docs/advanced/extension.rst

@@ -159,8 +159,9 @@ These two templates determine how to populate the config template based on the l
             return self.template.format(**params)
 
 Now, we need to tell hls4ml about the existence of this new layer by registering it.
-We also need to register the parser (a.k.a. the layer handler), the template passes, and the HLS implementation source code with the particular backend.
-In this case, the HLS code is valid for both the Vivado and Quartus backends.
+We also need to register the parser (a.k.a. the layer handler), the
+template passes, and the HLS implementation source code with the particular backend.
+In this case, the HLS code is valid for the Vivado backend.
 
 .. code-block:: Python
 
@@ -171,16 +172,14 @@ In this case, the HLS code is valid for both the Vivado and Quartus backends.
     # Register the hls4ml's IR layer
     hls4ml.model.layers.register_layer('KReverse', HReverse)
 
-    for backend_id in ['Vivado', 'Quartus']:
-        # Register the optimization passes (if any)
-        backend = hls4ml.backends.get_backend(backend_id)
+    backend = hls4ml.backends.get_backend('Vivado')
 
-        # Register template passes for the given backend
-        backend.register_template(HReverseConfigTemplate)
-        backend.register_template(HReverseFunctionTemplate)
+    # Register template passes for the backend
+    backend.register_template(HReverseConfigTemplate)
+    backend.register_template(HReverseFunctionTemplate)
 
-        # Register HLS implementation
-        backend.register_source('/path/to/your/nnet_reverse.h')
+    # Register HLS implementation
+    backend.register_source('/path/to/your/nnet_reverse.h')
 
 Finally, we can actually test the ``hls4ml`` custom layer compared to the Keras one.
 
@@ -198,17 +197,15 @@ Finally, we can actually test the ``hls4ml`` custom layer compared to the Keras
     x = np.random.randint(-5, 5, (8,), dtype='int32')
     kres = kmodel(x)
 
-    for backend_id in ['Vivado', 'Quartus']:
-
-        hmodel = hls4ml.converters.convert_from_keras_model(
-            kmodel,
-            output_dir=str(f'hls4mlprj_extensions_{backend_id}'),
-            backend=backend_id,
-            io_type='io_parallel',
-            hls_config={'Model': {'Precision': 'ap_int<6>', 'ReuseFactor': 1}},
-        )
+    hmodel = hls4ml.converters.convert_from_keras_model(
+        kmodel,
+        output_dir='hls4mlprj_extensions_Vivado',
+        backend='Vivado',
+        io_type='io_parallel',
+        hls_config={'Model': {'Precision': 'ap_int<6>', 'ReuseFactor': 1}},
+    )
 
-        hmodel.compile()
-        hres = hmodel.predict(x.astype('float32'))
+    hmodel.compile()
+    hres = hmodel.predict(x.astype('float32'))
 
-        np.testing.assert_array_equal(kres, hres)
+    np.testing.assert_array_equal(kres, hres)

docs/index.rst

@@ -39,7 +39,6 @@
     backend/altera
     backend/vitis_unified
     backend/catapult
-    backend/quartus
     backend/sr
     backend/xls
 

docs/intro/setup.rst

@@ -70,8 +70,6 @@ To run FPGA synthesis, installation of the following tools is required:
 
 * Vitis HLS 2023.1 or newer is required for synthesis for Xilinx FPGAs using the ``Vitis`` backend.
 
-* Intel Quartus 20.1 to 21.4 for synthesis for Intel/Altera FPGAs using the ``Quartus`` backend.
-
 * Altera HLS IP Gen (version 2026.1.0 tested) and recent Intel/Altera Quartus for Intel/Altera FPGAs using the ``Altera`` backend.
 
 Catapult HLS 2024.1_1 or 2024.2 can be used to synthesize both for ASICs and FPGAs.
@@ -204,9 +202,6 @@ Optional Dependencies
    # For QKeras-v3 frontend
    pip install hls4ml[qkeras-v3]
 
-   # For Quartus report parsing
-   pip install hls4ml[quartus-report]
-
    # For snnTorch frontend
    pip install hls4ml[snn]