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1.9.1
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ZigZag - Deep Learning Hardware Design Space Exploration
This repository presents the novel version of our tried-and-tested hardware Architecture-Mapping Design Space Exploration (DSE) Framework for Deep Learning (DL) accelerators. ZigZag bridges the gap between algorithmic DL decisions and their acceleration cost on specialized accelerators through a fast and accurate hardware cost estimation.
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This is the complete list of members for ImcArray, including all inherited members.
| __eq__(self, Any other) | ImcArray | |
| __init__(self, bool is_analog_imc, int bit_serial_precision, list[int] input_precision, int adc_resolution, int cells_size, float|None cells_area, dict[OADimension, int] dimension_sizes, bool auto_cost_extraction=False) | ImcArray | |
| __jsonrepr__(self) | ImcArray | |
| activation_precision | ImcArray | |
| adc_resolution | ImcArray | |
| area | ImcArray | |
| area_breakdown | ImcArray | |
| bit_serial_precision | ImcArray | |
| cells_area | ImcArray | |
| cells_size | ImcArray | |
| cells_w_cost | ImcArray | |
| dimension_sizes | ImcArray | |
| energy | ImcArray | |
| energy_breakdown | ImcArray | |
| get_adc_cost(self) | ImcArray | |
| get_area(self) | ImcArray | |
| get_dac_cost(self) | ImcArray | |
| get_energy_for_a_layer(self, LayerNode layer, Mapping mapping) | ImcArray | |
| get_macro_level_peak_performance(self) | ImcArray | |
| get_peak_energy_single_cycle(self) | ImcArray | |
| get_tclk(self) | ImcArray | |
| mapped_rows_total_per_macro | ImcArray | |
| tclk | ImcArray | |
| tclk_breakdown | ImcArray | |
| weight_precision | ImcArray |
1.9.1