1. Data Parallelism Approach: Caffe follows a data parallelism approach where the computation graph is static and predefined, while Chainer supports dynamic computation graphs allowing for more flexibility in model development. This difference allows Chainer to have a more intuitive and flexible approach to defining and modifying models during training.

2. Tensor Representation: Caffe represents tensors as blobs with fixed shapes and relies on a static computation graph, while Chainer treats data as multi-dimensional arrays and allows dynamic graph construction. This difference in tensor representation impacts the ease of handling variable input sizes and shapes in Chainer compared to Caffe.

3. Development Language: Caffe is primarily developed in C++ and utilizes a C++ API, whereas Chainer is developed in Python and provides Python bindings. This difference can affect the ease of use and integration with other Python-based libraries and tools for machine learning tasks.

4. GPU Integration: Both Caffe and Chainer offer GPU support for accelerated training, but Chainer provides more seamless integration with CUDA libraries for GPU computations. This distinction can lead to differences in performance and efficiency when running models on GPU hardware.

5. Community Support: Caffe has a large and established community of users and contributors, while Chainer has a smaller but rapidly growing community. This variance in community size can impact the availability of resources, documentation, and community-driven extensions for both frameworks.

6. Model Deployment: Caffe is often preferred for deployment in production environments due to its efficient inference capabilities and support for model optimization techniques, whereas Chainer is more commonly used during the research and development phase due to its flexibility and ease of experimentation with models. This difference in focus affects the suitability of each framework for different stages of the machine learning pipeline.

In Summary, the key differences between Caffe and Chainer lie in their approach to data parallelism, tensor representation, development language, GPU integration, community support, and model deployment.