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インテルFPGAのDeep Learning Acceleration SuiteとマイクロソフトのBrainwaveをHW視点から比較してみる MS編

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インテルFPGAのDeep Learning Acceleration SuiteとマイクロソフトのBrainwaveをHW視点から比較してみる
インテルFPGAのDeep Learning Acceleration SuiteとマイクロソフトのBrainwaveは、どちらもFPGAを用いてインファレンス処理を行うものでありながら、その内部構成は180度異なります。ここでは両者を比較しつつ、インテルのインファレンスソリューションについて解説します。

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インテルFPGAのDeep Learning Acceleration SuiteとマイクロソフトのBrainwaveをHW視点から比較してみる MS編

  1. 1. Catapult v0 Catapult v1 スケール v1 Catapult v2 2011 2012 2013 2014 2015 2016 ... Ignite 本番展開
  2. 2. Web search ranking Traditional software (CPU) server plane QPICPU QSFP 40Gb/s ToR FPGA CPU 40Gb/s QSFP QSFP Hardware acceleration plane Web search ranking Deep neural networks SDN offload SQL CPUs FPGAs Routers
  3. 3. f f f l0 l1 f f f l0 Pretrained DNN モデル CNTK などで スケーラブルな DNN ハードウェア マイクロサービス BrainWave Soft DPU Instruction Decoder & Ctrl Neural FU ネットワークスイッチ FPGA
  4. 4. Pretrained DNN モデル を ソフト DPU にコンパイルするための フレームワーク中立の連合コンパイラとランタイム 狭精度 DNN 推論のための適応型 ISA 変化目まぐるしいAI アルゴリズムをサポートする柔軟性と拡張性 BrainWave Soft DPU マイクロアーキテクチャ 高精度、低遅延バッチに最適 Intel の FPGA を スケールする HW マイクロサービスに展開 [マイクロ ' 16] FPGA 上でモデルパラメータを完全に永続化するオンチップメモリは、 多数の FPGA にまたがってスケーリングすることにより、 大規模なモデルをサポート
  5. 5. FPGA0 FPGA1 Add500 1000-dim ベクトル 1000-dim ベクトル 分割 500x500 マトリックス MatMul500 500x500 マトリックス MatMul500 MatMul500 MatMul500 500x500 マトリックス Add500 Add500 Sigmoid500 Sigmoid500 分割 Add500 500 500 concat 500 500 500x500 マトリックス ターゲット コンパイラ FPGA ターゲット コンパイラ CPU-CNTK フロント ポータブル IR ターゲット コンパイラ CPU-カフェ トランスフォーム IRs グラフスプリッタ と オプティマイザ 展開パッケージ Caffe モデル FPGA ハードウェア マイクロサービス CNTK モデル Tensorflow モデル
  6. 6. = O(N2) data O(N2) compute 入力アクティベーション 出力前のアクティベーション N ウェイトカーネル O(N3) data O(N4K2) compute =
  7. 7. FPGA2xCPU DRAM で初期化された モデルパラメータ
  8. 8. FPGA2xCPU DRAM で初期化された モデルパラメータ
  9. 9. バッチサイズ ハードウェア 利用 (%) FPGA
  10. 10. バッチサイズ 99回目 待ち時 間 最大 許可 遅延 バッチサイズ ハードウェア 利用 (%) バッチ処理により HW の使用率が向上するが、待ち時間は増加
  11. 11. バッチサイズ 99回目 の待ち 時間 最大 許可 遅延 バッチサイズ ハードウェア 利用 (%) バッチ処理により HW の使用率が向上するが、待ち時間が増加
  12. 12. FPGA2xCPU
  13. 13. 2xCPU 観測
  14. 14. 2xCPU
  15. 15. 2xCPU
  16. 16. FPGA MVU カーネル + + × × + × × + + × × + × × +

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