Fix links in documentation (attempt 2)

Change-Id: I631acc646d11cf6034d26e2880e220ca8408865d

Author: tom-arm

docs/source/backends/arm-ethos-u/arm-ethos-u-overview.md

@@ -85,15 +85,15 @@ Returns a constant string that is the output format of the class.
 
 ### Partitioner API
 
-See [Partitioner API](backends/arm-ethos-u/arm-ethos-u-partitioner.md) for more information of the Partitioner API.
+See [Partitioner API](/backends/arm-ethos-u/arm-ethos-u-partitioner) for more information of the Partitioner API.
 
 ## Quantization
 
 Since the Ethos-U backend is integer-only, all operators intended be executed on the NPU needs to be quantized. The Ethos-U quantizer supports
 [Post Training Quantization (PT2E)](https://docs.pytorch.org/ao/main/tutorials_source/pt2e_quant_ptq.html)  and
 [Quantization-Aware Training (QAT)](https://docs.pytorch.org/ao/main/tutorials_source/pt2e_quant_qat.html) quantization.
 
-For more information on quantization, see [Quantization](backends/arm-ethos-u/arm-ethos-u-quantization)
+For more information on quantization, see [Quantization](/backends/arm-ethos-u/arm-ethos-u-quantization)
 
 ## Runtime Integration
 

docs/source/backends/arm-vgf/arm-vgf-overview.md

@@ -77,18 +77,18 @@ Returns a constant string that is the output format of the class.
 
 ### Partitioner API
 
-See [Partitioner API](backends/arm-vgf/arm-vgf-partitioner.md) for more information of the Partitioner API.
+See [Partitioner API](/backends/arm-vgf/arm-vgf-partitioner) for more information of the Partitioner API.
 
 ## Quantization
 
 The VGF quantizer supports [Post Training Quantization (PT2E)](https://docs.pytorch.org/ao/main/tutorials_source/pt2e_quant_ptq.html)
 and [Quantization-Aware Training (QAT)](https://docs.pytorch.org/ao/main/tutorials_source/pt2e_quant_qat.html).
 
-For more information on quantization, see [Quantization](backends/arm-vgf/arm-vgf-quantization).
+For more information on quantization, see [Quantization](/backends/arm-vgf/arm-vgf-quantization).
 
 ## Runtime Integration
 
-The VGF backend can use the default ExecuTorch runner. The steps required for building and running it are explained in the [VGF Backend Tutorial](/backends/arm-vgf/tutorials/vgf-getting-started.md).
+The VGF backend can use the default ExecuTorch runner. The steps required for building and running it are explained in the [VGF Backend Tutorial](/backends/arm-vgf/tutorials/vgf-getting-started).
 The example application is recommended to use for testing basic functionality of your lowered models, as well as a starting point for developing runtime integrations for your own targets.
 
 ## Reference