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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"! core computation equation, e.g. More...
Public Member Functions | |
def | __init__ (self, str data) |
list[LayerOperand] | get_contained_operands (self) |
Return a list with all LayerOperands contained within this instance. More... | |
list[LayerDim] | get_r_layer_dims (self, LayerOperand layer_op) |
Return a list with all LayerDims that are relevant for the given LayerOperand. More... | |
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def | __init__ (self, Any data) |
int | __len__ (self) |
Iterator[Any] | __iter__ (self) |
def | __getitem__ (self, Any key) |
bool | __contains__ (self, Any key) |
def | __str__ (self) |
def | __repr__ (self) |
Any | __jsonrepr__ (self) |
def | __eq__ (self, object other) |
def | __hash__ (self) |
Public Attributes | |
data | |
disassembly | |
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data | |
"! core computation equation, e.g.
O[g][b][k][oy][ox]+=W[g][k][c][fy][fx]*I[g][b][c][ix][iy]
, Y[i][j] += A[i][k] * B[k][j]
, Y[i][j] += A[i][k][l] * B[k][j] * C[l][j]
, etc.
def __init__ | ( | self, | |
str | data | ||
) |
list[LayerOperand] get_contained_operands | ( | self | ) |
Return a list with all LayerOperands contained within this instance.
list[LayerDim] get_r_layer_dims | ( | self, | |
LayerOperand | layer_op | ||
) |
Return a list with all LayerDims that are relevant
for the given LayerOperand.
data |
disassembly |