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深層学習の新しい応用と、 それを支える計算機の進化 - Preferred Networks CEO 西川徹 (SEMICON Japan 2022 Keynote)
- 21. 深層学習登場
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Petaflops/s-days
深層学習の登場で
必要な計算力は
加速度的に増加
3~4ヶ月ごとに倍増
AlphaGoZero
Neural Machine
Translation
T17 Dota1v1
ResNets
VGG
VGG
AlexNet
DQN
Deep Belief Nets and
layer-wise pretraining
TD-Gammon v2.1
BILSTM for Speech
Lenet-5
RNN forSpeech
ALVINN
NETtalk
Perceptron
出典: OpenAI. Two Distinct Eras of Compute Usage in Training AI Systems
- 29. スーパーなコンパイラで汎用性と性能の両立を実現
MNGraph
L3IR
DNN op level,
SIMD parallelism
strategy, …...
Global
Layout
Planner
Re-
computation
Scheduler
L2IR
ndarray op level,
optimized DNN op
impl, …...
Generic Conv
Impl.
MNTensor
PEVector
Reshape Impl.
L1IR
MN-core op level,
memory allocation,
scheduling,
optimization, …...
Layer Impl.
Scheduling
Graph
Instruction
Merger
ONNX model