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

@@ -160,7 +160,7 @@ These two templates determine how to populate the config template based on the l
 
 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 HLS implementation source code with the particular backend.
-In this case, the HLS code is valid for both the Vivado and Quartus backends.
+In this case, the HLS code is valid for the Vivado backend.
 
 .. code-block:: Python
 
@@ -171,16 +171,14 @@ 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 +196,15 @@ 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/backend/oneapi.rst

@@ -2,9 +2,8 @@
 oneAPI
 ======
 
-The **oneAPI** backend of hls4ml is designed for deploying NNs on Intel/Altera FPGAs. It will eventually
-replace the **Quartus** backend, which targeted Intel HLS. (Quartus continues to be used with IP produced by the
-**oneAPI** backend.) This section discusses details of the **oneAPI** backend.
+The **oneAPI** backend of hls4ml is designed for deploying NNs on Intel/Altera FPGAs. Quartus continues to be used
+with IP produced by the **oneAPI** backend. This section discusses details of the **oneAPI** backend.
 
 The **oneAPI** code uses SYCL kernels to implement the logic that is deployed on FPGAs. It naturally leads to the
 accelerator style of programming. In the SYCL HLS (IP Component) flow, which is currently the only flow supported, the
@@ -18,8 +17,8 @@ produces the library used for calling the ``predict`` function from hls4ml. The
 in hls4ml interact with the cmake system, so one does not need to manually use the build system, but it there
 if desired.
 
-The **oneAPI** backend, like the **Quartus** backend, only implements the ``Resource`` strategy for the layers. There
-is no ``Latency`` implementation of any of the layers.
+The **oneAPI** backend only implements the ``Resource`` strategy for the layers. There is no ``Latency``
+implementation of any of the layers.
 
 Note:  currently tracing and external weights (i.e. setting BramFactor) are not supported.
 
@@ -30,6 +29,6 @@ As mentioned in the :ref:`I/O Types` section, ``io_parallel`` is for small model
 larger models. In ``oneAPI``, there is an additional difference: ``io_stream`` implements each layer on its
 own ``task_sequence``. Thus, the layers run in parallel, with pipes connecting the inputs and outputs. This
 is similar in style to the `dataflow` implementation on Vitis HLS, but more explicit. It is also a change
-relative to the Intel HLS-based ``Quartus`` backend. On the other hand, ``io_parallel`` always uses a single task,
+relative to the former Intel HLS-based implementation. On the other hand, ``io_parallel`` always uses a single task,
 relying on pipelining within the task for good performance. In contrast, the Vitis backend sometimes uses dataflow
 with ``io_parallel``.

docs/index.rst

@@ -37,7 +37,6 @@
     backend/accelerator
     backend/oneapi
     backend/catapult
-    backend/quartus
     backend/sr
     backend/xls
 

docs/intro/setup.rst

@@ -70,9 +70,7 @@ To run FPGA synthesis, installation of 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 the synthesis for Intel/Altera FPGAs using the ``Quartus`` backend.
-
-* oneAPI 2024.1 to 2025.0 with the FPGA compiler and recent Intel/Altera Quartus for Intel/Altera FPGAs using the ``oneAPI`` backend. Newer versions of ``OneAPI`` removed FPGA support and **will to work** with ``hls4ml``.
+* oneAPI 2024.1 to 2025.0 with the FPGA compiler and recent Intel/Altera Quartus for Intel/Altera FPGAs using the ``oneAPI`` backend. Newer versions of ``oneAPI`` removed FPGA support and **will not work** with ``hls4ml``.
 
 Catapult HLS 2024.1_1 or 2024.2 can be used to synthesize both for ASICs and FPGAs.
 
@@ -201,9 +199,6 @@ Optional Dependencies
    # For QKeras-v3 frontend
    pip install hls4ml[qkeras-v3]
 
-   # For Quartus report parsing
-   pip install hls4ml[quartus-report]
-
    # For symbolic regression
    pip install hls4ml[sr]