<jupyter_start><jupyter_text>More examples with larger hardware setups and problems  We provide more examples on realisitic DNN layers and hardware setups Exercise 04.2.1 Eyeriss like designWe include Eyeriss architecture with offchip compression and onchip gating based on input activation's sparsity Examine Sparse OptimizationWe introduce a new specification key  called `flattened_rankIDs`, whcih specifies which dimension can be flattened together and indexed with one specific type of metadata. Each list in the `flattened_rankIDs` specification indicates the set of dimensions that can be flattened together. For example, `RLE` can be applied to a large tile of multidimensional data that's falttened into a single diemension.<jupyter_code>%%bash
cd ../designs/04.2.1-eyeriss-like-gating/
cat sparse_opt/*.yaml
%%bash
cd ../designs/04.2.1-eyeriss-like-gating/scripts
./run.sh<jupyter_output><empty_output><jupyter_text>Exercise 04.2.2 Eyeriss like design with mapspace seachWe furthur illustrate the potential of mapspace search for a sparse design and a sparse problem with `timeloop-mapper`The search might take a while (<5min), and we recommend using the terminal to run the example, since the progress prints do not show in jupyter notebook cells<jupyter_code>%%bash
cd ../designs/04.2.2-eyeriss-like-gating-mapspace-search/scripts
chmod +x run.sh
./run.sh<jupyter_output><empty_output><jupyter_text>Exercise 04.2.3 Eyeriss like design with onchip compressionWe furthur enhance the design to allow onchip compression of input activations and skippingThe search might take a while (<5min), and we recommend using the terminal to run the example, since the progress prints do not show in jupyter notebook cellsNote that, with skipping, we can enhance the utilization of the design.<jupyter_code>%%bash
cd ../designs/04.2.3-eyeriss-like-onchip-compression/scripts
chmod +x run.sh
./run.sh<jupyter_output><empty_output>
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