ウェブブラウザ向け深層学習モデル高速実行フレームワーク「WebDNN」

2017/09/03 Deep Learning Acceleration勉強会@DeNAでの発表資料です。

Transcript

1. ΢Σϒϒϥ΢β޲͚ਂ૚ֶशϞσϧߴ଎࣮ߦϑϨʔϜϫʔΫ WebDNN ౦ژେֶ େֶӃ৘ใཧ޻ֶܥݚڀՊ ૑଄৘ใֶઐ߈ ݪాɾڇٱݚڀࣨ म࢜೥ ໦૔ ༔Ұ࿠ !,JJLVSBHF
2. ໦૔ ༔Ұ࿠:VJDIJSP
   ,JLVSB ౦ژେֶ େֶӃ৘ใཧ޻ֶܥݚڀՊ ૑଄৘ใֶઐ߈ ݪాɾڇٱݚڀࣨ म࢜೥ ΢Σϒϒϥ΢β޲͚ਂ૚ֶशϞσϧߴ଎࣮ߦϑϨʔϜϫʔΫʮ8FC%//ʯͷ͝঺հ • 8FC%//ͱ͸ • 8FC%//ͷ࢖͍ํ • 8FC%//͕ߦ͍ͬͯΔߴ଎ԽΞϓϩʔν ͸͡Ίʹ
3. 8FC%// ΢Σϒϒϥ΢β޲͚ਂ૚ֶशϞσϧߴ଎࣮ߦϑϨʔϜϫʔΫ ֶशࡁΈϞσϧΛਪ࿦ϑΣʔζʹ࠷దԽ͠΢Σϒϒϥ΢β্Ͱ࣮ߦ 5IJT
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4. 8FC%// ΢Σϒϒϥ΢β޲͚ਂ૚ֶशϞσϧߴ଎࣮ߦϑϨʔϜϫʔΫ ֶशࡁΈϞσϧΛਪ࿦ϑΣʔζʹ࠷దԽ͠΢Σϒϒϥ΢β্Ͱ࣮ߦ Ϟσϧఆٛ ม ׵ ࠷ ద Խ ੜ ੒ (16
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5. 8FC%//ͷར༻ྫ .BLF(JSMT.PF <:
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6. ΢Σϒϒϥ΢βͰͷਂ૚ֶश %//Λ࣮ΞϓϦέʔγϣϯ΁Ԡ༻͢Δࡍͷ՝୊ αʔόʔαΠυίϯϐϡʔςΟϯά • ΫϥΠΞϯτ਺ʹର͢ΔεέʔϧΞ΢τ͕༰қͰ͸ͳ͍ • ϓϥΠόγʔʹ఍৮͢ΔσʔλΛѻ͍ͮΒ͍ ΫϥΠΞϯταΠυίϯϐϡʔςΟϯά • ܭࢉ؀ڥηοτΞοϓ͕೉͍͠
7. ΢Σϒϒϥ΢βͰͷਂ૚ֶश %//Λ࣮ΞϓϦέʔγϣϯ΁Ԡ༻͢Δࡍͷ՝୊ ΢ΣϒΞϓϦέʔγϣϯ • ΫϥΠΞϯτଆͰܭࢉ͢ΔͨΊεέʔϧΞ΢τ͕༰қ • αʔόʔ΁ͷσʔλΞοϓϩʔυ͕ෆཁͳͨΊಗ໊ੑͷ୲อ͕༰қ • ηοτΞοϓ͸ෆཁ ΢ΣϒαΠτʹΞΫηε͢Δ͚ͩͰར༻Մೳ "JYJMF͞Μ !OBNBOJLV .BLF(JSTU.PFͷ࡞ऀ
8. ΢Σϒϒϥ΢βͰͷػցֶश • .*-+4 IUUQNJMUPLZPHJUIVCJPNJMKTIUNM ౰ݚڀࣨͰ։ൃ͍ͯ͠Δ+BWB4DSJQUϕʔεػցֶशϥΠϒϥϦ܈ • 4VTIJ +BWB4DSJQUͰಈ࡞͢ΔߦྻԋࢉϥΠϒϥϦ <,
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9. 8FC%//ͷಛ௃  ΢Σϒ࠷৽࢓༷Λ༻͍ͨߴ଎ͳܭࢉόοΫΤϯυ 8FC(16ɾ8FC(-ɾ8FC"TTFNCMZʹΑΔ$16(16྆ํͰͷߴ଎ͳ࣮ߦ  ܭࢉάϥϑ࠷దԽʹΑΔܭࢉͷߴ଎Խ ਪ࿦ϑΣʔζʹಛԽͨ͠࠷దԽʹΑΓܭࢉྔɾϞσϧύϥϝʔλαΠζΛ࡟ݮ  طଘͷਂ૚ֶशϑϨʔϜϫʔΫͷ޿ൣғͳαϙʔτ ,FSBT
   $BGGF
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10. 8FC%//ͷಛ௃  ΢Σϒ࠷৽࢓༷Λ༻͍ͨߴ଎ͳܭࢉόοΫΤϯυ • ༷ʑͳ؀ڥʹରԠՄೳͳछྨͷܭࢉόοΫΤϯυΛ࣮૷ • ϒϥ΢βʹ࣮૷͞Ε͍ͯΔ"1*ʹԠͯࣗ͡ಈతʹ࠷దͳόοΫΤϯυΛબ୒ • ΄΅શͯͷ؀ڥͰ(16ʹΑΔϋʔυ΢ΣΞΞΫηϥϨʔγϣϯ͕ར༻Մೳ 8FC(16 #BDLFOE 8FC(- #BDLFOE 8FC"TTFNCMZ #BDLFOE 'BMMCBDL #BDLFOE
11. 8FC%//ͷಛ௃  ܭࢉάϥϑ࠷దԽʹΑΔܭࢉͷߴ଎Խ • ਪ࿦ϑΣʔζʹಛԽ͢Δ͜ͱͰɺΑΓੵۃతͳܭࢉάϥϑͷ࠷దԽΛ࣮ࢪ
12. 8FC%//ͷಛ௃  طଘͷਂ૚ֶशϑϨʔϜϫʔΫͷ޿ൣғͳαϙʔτ • ,FSBT
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13. ϕϯνϚʔΫ 3FT/FU<)F
    > ը૾ຕͷਪ࿦ʹཁ͢Δ࣌ؒ • ೖྗαΠζ • ൺֱର৅ • ,FSBTKT IUUQTHJUIVCDPNUSBOTDSBOJBMLFSBTKT LFSBTͷֶशࡁΈϞσϧΛ࣮ߦՄೳͳϥΠϒϥϦɻ8FC(-ʹΑΔ(16ར༻ɻ (1,224,224,3)
14. ϕϯνϚʔΫ 3FT/FUը૾ຕͷਪ࿦ʹཁ͢Δ࣌ؒ       ,FSBTKT 8FC%// ॴཁ࣌ؒ <NTຕ> $ISPNF 'JSF'PY 4BGBSJ
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17. ࢖༻ํ๏ 
    ֶशࡁΈϞσϧΛม׵͢Δ
    ,FSBT from keras.applications import resnet50 from webdnn.frontend.keras import KerasConverter from webdnn.backend import generate_descriptor graph = KerasConverter().convert(resnet50.ResNet50()) # (1) generate_descriptor('webgl', graph).save('model') # (2)   ,FSBT keras.Model (SBQI%FTDSJQUPS GPS
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18. ࢖༻ํ๏ 
    ֶशࡁΈϞσϧΛม׵͢Δ
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    ܭࢉάϥϑΛ࡞ΔͨΊʹμϛʔσʔλΛྲྀ͢ x = Variable(np.zeros((1, 3, 224, 224), dtype=np.float32)) y = model(x, layers=['prob'])['prob'] $IBJOFS chainer.Chain (SBQI%FTDSJQUPS GPS
    8FC(- #BDLFOE   8FC%//
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19. ࢖༻ํ๏ 
    ֶशࡁΈϞσϧΛม׵͢Δ
    $IBJOFS from webdnn.frontend.chainer import ChainerConverter from webdnn.backend import generate_descriptor graph = ChainerConverter().convert([x], [y]) # (1) generate_descriptor('webgl', graph).save('model') # (2) $IBJOFS chainer.Chain (SBQI%FTDSJQUPS GPS
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20. ࢖༻ํ๏ 
    ม׵݁ՌΛϒϥ΢β͔ΒಡΈࠐΈ࣮ߦ͢Δ let runner = await WebDNN.load('model'); let x = runner.getInputViews()[0]; let y = runner.getOutputViews()[0];  جຊతͳલɾޙॲཧʹؔ͢ΔαϙʔτϥΠϒϥϦ͕෇ଐ x.set(await WebDNN.Image.getImageArray('cat.jpg', { dstW: 224, dstH: 224 })); await runner.run(); 
    ࣮ߦ console.log(y.toActual()); JavaScript +BWB4DSJQU
21. ߴ଎Խ ΢Σϒϒϥ΢βίϯϐϡʔςΟϯάʹ͓͍ͯ཯଎ͱͳΔཁҼ +BWB4DSJQUͷΦʔόʔϔου ԋࢉίΞෆ଍ ϝϞϦଳҬෆ଍
22. ߴ଎Խ ΢Σϒϒϥ΢βίϯϐϡʔςΟϯάʹ͓͍ͯ཯଎ͱͳΔཁҼ +BWB4DSJQUͷΦʔόʔϔου ԋࢉίΞෆ଍ ϝϞϦଳҬෆ଍
23. +BWB4DSJQUͷΦʔόʔϔου • +BWB4DSJQUͦͷ΋ͷͷ࣮ߦ଎౓͕஗͍ • ௿ϨΠϠΛ৮ΕΔ"1*͕૿͖͑ͯͨ • 7BOJMMB
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24. 8FC"TTFNCMZ ΢Σϒϒϥ΢β͔ΒΞηϯϒϥϨϕϧͰͷ໋ྩΛѻ͑Δ࢓༷ • +BWB4DSJQUͷΠϯλϓϦλ࣮ߦʹΑΔΦʔόʔϔου͕ͳͤ͘Δ • 7BOJMMB
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25. 8FC(- • ΢Σϒϒϥ΢βʹ͸(1(16༻"1*͕ଘࡏ͠ͳ͍ • 8FC(- • ը૾ॲཧ༻"1*Ͱ͋Γ$PNQVUJOH
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26. 8FC(16 • IUUQTXFCLJUPSHXQDPOUFOUVQMPBETXFCHQVBQJQSPQPTBMIUNM • "QQMF͕த৺ͱͳͬͯٞ࿦தͷ࣍ظ"1* • $PNQVUJOH
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27. ߴ଎Խ ΢Σϒϒϥ΢βίϯϐϡʔςΟϯάʹ͓͍ͯ཯଎ͱͳΔཁҼ +BWB4DSJQUͷΦʔόʔϔου ԋࢉίΞෆ଍ ϝϞϦଳҬෆ଍
28. ϝϞϦଳҬෆ଍ͷରࡦ ,FSOFM
    .FSHJOH • ແବͳಡΈॻ͖ΛݮΒ͠ԋࢉີ౓ΛߴΊ • ෳ਺ͷΧʔωϧΛ̍ͭʹϚʔδ͢Δ • ൃߦͷΦʔόʔϔου΋ݮΒͤΔ float x1 = X1[i]; float x2 = X2[i]; H[i] = x1 + x2; float h = H[i]; Y[i] = h>0?h:0; float x1 = X1[i]; float x2 = X2[i]; float h = x1 + x2; Y[i] = h>0?h:0; 3FBE 3FBE 3FBE 8SJUF 8SJUF 3FBE 3FBE 8SJUF
29. άϥϑߏ଄࠷దԽ ࣄલʹొ࿥͞ΕͨύλʔϯʹԊͬͯάϥϑΛม׵ $POTUBOU
    'PMEJOH Add Var Const1 Const2 Const1 + Const2 ύλʔϯ ม׵ ఆ਺ ม਺
30. άϥϑߏ଄࠷దԽ ࣄલʹొ࿥͞ΕͨύλʔϯʹԊͬͯάϥϑΛม׵ Add x1 Add x3 x2 c1 c2
31. άϥϑߏ଄࠷దԽ ࣄલʹొ࿥͞ΕͨύλʔϯʹԊͬͯάϥϑΛม׵ Add x1 Add x3 x2 c1 c2 $POTUBOU
    'PMEJOH Add x3 x2 c1 + c2
32. &YQSFTTJPO
    4JNQMJGJDBUJPO ఆ਺߲Λ·ͱΊɺࣜΛ୯७Խ͠ଞͷ࠷దԽΛ༰қʹ͢Δ x1 c1 c2 Add Add x2 Add (x1 + c1) + (x2 + c2)
33. &YQSFTTJPO
    4JNQMJGJDBUJPO ఆ਺߲Λ·ͱΊɺࣜΛ୯७Խ͠ଞͷ࠷దԽΛ༰қʹ͢Δ x1 x2 c2 Add Add c1 Add (x1 + c1) + (x2 + c2) = (x1 + x2) + (c1 + c2)
34. &YQSFTTJPO
    4JNQMJGJDBUJPO ఆ਺߲Λ·ͱΊɺࣜΛ୯७Խ͠ଞͷ࠷దԽΛ༰қʹ͢Δ x1 x2 c2 Add Add c1 Add $POTUBOU
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35. &YQSFTTJPO
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36. /PSNBMJ[BUJPO &YQSFTTJPO
    4JNQMJGJDBUJPO ఆ਺߲Λ·ͱΊɺࣜΛ୯७Խ͠ଞͷ࠷దԽΛ༰қʹ͢Δ x1 w Conv2D x2 s Mul x3 b Add
37. &YQSFTTJPO
    4JNQMJGJDBUJPO ఆ਺߲Λ·ͱΊɺࣜΛ୯७Խ͠ଞͷ࠷దԽΛ༰қʹ͢Δ x1 w Conv2D x2 s Mul x3 b Add &YQSFTTJPO
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38. &YQSFTTJPO
    4JNQMJGJDBUJPO ఆ਺߲Λ·ͱΊɺࣜΛ୯७Խ͠ଞͷ࠷దԽΛ༰қʹ͢Δ x1 Conv2D x3 b Add x4 s Mul w
39. &YQSFTTJPO
    4JNQMJGJDBUJPO ఆ਺߲Λ·ͱΊɺࣜΛ୯७Խ͠ଞͷ࠷దԽΛ༰қʹ͢Δ x1 Conv2D x3 b Add x4 s Mul w $POTUBOU
    'PMEJOH
40. &YQSFTTJPO
    4JNQMJGJDBUJPO ఆ਺߲Λ·ͱΊɺࣜΛ୯७Խ͠ଞͷ࠷దԽΛ༰қʹ͢Δ x1 Conv2D x3 b Add w * s
41. ,FSOFM
    .FSHJOH &MFNFOUXJTFͳΧʔωϧΛ̍ͭʹ·ͱΊɺϝϞϦ΁ͷॻ͖ࠐΈ΍ ΧʔωϧͷσΟεύονʹΑΔΦʔόʔϔουΛݮΒ͢ Conv2D x2 Add Mul Add x3 x4 x9 Conv2D x6 Mul Add x7 x8 x10 Relu s1 b1 b2 s2 x1 w1 x5 w2
42. ,FSOFM
    .FSHJOH &MFNFOUXJTFͳΧʔωϧΛ̍ͭʹ·ͱΊɺϝϞϦ΁ͷॻ͖ࠐΈ΍ ΧʔωϧͷσΟεύονʹΑΔΦʔόʔϔουΛݮΒ͢ Conv2D x2 Add Mul Add x3 x4 x9 Conv2D x6 Mul Add x7 x8 x10 Relu s1 b1 b2 s2 x1 w1 x5 w2 &MFNFOUXJTF
43. &MFNFOUXJTF ,FSOFM
    .FSHJOH &MFNFOUXJTFͳΧʔωϧΛ̍ͭʹ·ͱΊɺϝϞϦ΁ͷॻ͖ࠐΈ΍ ΧʔωϧͷσΟεύονʹΑΔΦʔόʔϔουΛݮΒ͢ Conv2D x2 Add Mul Add x3 x4 x9 Conv2D x6 Mul Add x7 x8 x10 Relu s1 b1 b2 s2 x1 w1 x5 w2 ճ 3FBEɾ8SJUF
44. &YQSFTTJPO
    4JNQMJGJDBUJPO ,FSOFM
    .FSHJOH &MFNFOUXJTFͳΧʔωϧΛ̍ͭʹ·ͱΊɺϝϞϦ΁ͷॻ͖ࠐΈ΍ ΧʔωϧͷσΟεύονʹΑΔΦʔόʔϔουΛݮΒ͢ Conv2D x2 Add Mul Add x3 x4 x9 Conv2D x6 Mul Add x7 x8 x10 Relu s1 b1 b2 s2 x1 w1 x5 w2 &YQSFTTJPO
    4JNQMJGJDBUJPO
45. ,FSOFM
    .FSHJOH &MFNFOUXJTFͳΧʔωϧΛ̍ͭʹ·ͱΊɺϝϞϦ΁ͷॻ͖ࠐΈ΍ ΧʔωϧͷσΟεύονʹΑΔΦʔόʔϔουΛݮΒ͢ Conv2D Add Add x3 x4 x9 Conv2D Add x7 x8 x10 Relu b1 b2 x1 w1 * s1 x5 w2 * s2
46. &YQSFTTJPO
    4JNQMJGJDBUJPO ,FSOFM
    .FSHJOH &MFNFOUXJTFͳΧʔωϧΛ̍ͭʹ·ͱΊɺϝϞϦ΁ͷॻ͖ࠐΈ΍ ΧʔωϧͷσΟεύονʹΑΔΦʔόʔϔουΛݮΒ͢ Conv2D Add Add x3 x4 x9 Conv2D Add x7 x8 x10 Relu b1 b2 x1 w1 * s1 x5 w2 * s2
47. $POTUBOU
    'PMEJOH ,FSOFM
    .FSHJOH &MFNFOUXJTFͳΧʔωϧΛ̍ͭʹ·ͱΊɺϝϞϦ΁ͷॻ͖ࠐΈ΍ ΧʔωϧͷσΟεύονʹΑΔΦʔόʔϔουΛݮΒ͢ Conv2D Add Add x3 x4 x9 Conv2D Add x8 x10 Relu b2 x1 w1 * s1 x5 w2 * s2 x7 b1
48. ,FSOFM
    .FSHJOH &MFNFOUXJTFͳΧʔωϧΛ̍ͭʹ·ͱΊɺϝϞϦ΁ͷॻ͖ࠐΈ΍ ΧʔωϧͷσΟεύονʹΑΔΦʔόʔϔουΛݮΒ͢ Conv2D Add Add x3 x4 x9 Conv2D x10 Relu x1 w1 * s1 x5 w2 * s2 x7 b1 + b2
49. ,FSOFM
    .FSHJOH &MFNFOUXJTFͳΧʔωϧΛ̍ͭʹ·ͱΊɺϝϞϦ΁ͷॻ͖ࠐΈ΍ ΧʔωϧͷσΟεύονʹΑΔΦʔόʔϔουΛݮΒ͢ Conv2D Add Add x3 x4 x9 Conv2D x10 Relu x1 w1 * s1 x5 w2 * s2 x7 b1 + b2 &MFNFOUXJTF
    ,FSOFM
    .FSHJOH
50. ,FSOFM
    .FSHJOH &MFNFOUXJTFͳΧʔωϧΛ̍ͭʹ·ͱΊɺϝϞϦ΁ͷॻ͖ࠐΈ΍ ΧʔωϧͷσΟεύονʹΑΔΦʔόʔϔουΛݮΒ͢ Conv2D MergedElementwise x3 Conv2D x10 x1 w1 * s1 x5 w2 * s2 x7 b1 + b2 ճ ˠ
    ճ 3FBEɾ8SJUF
51. ,FSOFM
    .FSHJOH ,FSOFM
    .FSHJOHʹΑΔ࠷దԽͷޮՌ 4BGBSJ
    51
    
    
    .BD
    #PPL
    1SP
    &BSMZ
     3FT/FU࣮ߦ࣌ؒ <NTJNBHF> WebGPU backend 最適化なし  WebGPU backend ,FSOFM
    .FSHJOH 
52. ϝϞϦଳҬෆ଍΁ͷରࡦ %BUB
    'PSNBU
    0QUJNJ[BUJPO ܭࢉάϥϑதͷม਺ΛɺϝϞϦ্ʹͲͷΑ͏ʹ֨ೲ͢Δ͔Λ࠷దԽ • %BUB
    0SEFS 0QFSBUPSͷछྨɾೖग़ྗม਺ͷܗঢ়ͳͲʹΑΓదͨ͠σʔλΦʔμʔ͸ҟͳ Δ • ͋Δ0QFSBUPS͸/)8$Ͱɺผͷ0QFSBUPS͸/$)8ͰσʔλΛѻ͍͍ͨɺ ͱ͍ͬͨ͜ͱ͕͋Δ • લޙͷ0QFSBUPS΋Ճຯͯ͠ೖग़ྗม਺ͷ%BUB 0SEFSΛܾΊΔ
53. ϝϞϦଳҬෆ଍΁ͷରࡦ %BUB
    'PSNBU
    0QUJNJ[BUJPO ܭࢉάϥϑதͷม਺ΛɺϝϞϦ্ʹͲͷΑ͏ʹ֨ೲ͢Δ͔Λ࠷దԽ • "MJHONFOU
    BOE
    1BEEJOH 4*.%໋ྩ΍(16ΧʔωϧதͰͷॲཧൣғͷ෼ׂׂΓ౰ͯʢ5JMJOHʣΛ༰қʹ ͢ΔͨΊʹߦྻͷαΠζɾΦϑηοτΛἧ͑Δ
54. ϝϞϦଳҬෆ଍΁ͷରࡦ ྫ (&..
    $"# ߦྻͷαΠζ͕λΠϧαΠζͰ ៉ྷʹׂΓ੾ΕΔΑ͏ ༧ΊύσΟϯά͓ͯ͘͠ " # $ 5JMF
55. ϝϞϦଳҬෆ଍΁ͷରࡦ ྫ (&..
    $"# ߦྻͷαΠζ͕λΠϧαΠζͰ ៉ྷʹׂΓ੾ΕΔΑ͏ ༧ΊύσΟϯά͓ͯ͘͠ " 1BEEJOH # $
56. %BUB
    'PSNBU 0QUJNJ[BUJPO %BUB
    'PSNBU 0QUJNJ[BUJPOʹΑΔ࠷దԽͷޮՌ 4BGBSJ
    51
    
    
    .BD
    #PPL
    1SP
    &BSMZ
     3FT/FU࣮ߦ࣌ؒ <NTJNBHF> WebGPU backend 最適化なし  WebGPU backend + Elementwise Kernel Merging  WebGPU backend + Elementwise Kernel Merging
    %BUB
    'PSNBU 0QUJNJ[BUJPO 
57. ·ͱΊ 8FC%//
    ΢Σϒϒϥ΢β޲͚ਂ૚ֶशϞσϧߴ଎࣮ߦϑϨʔϜϫʔΫ IUUQTNJMUPLZPHJUIVCJPXFCEOO  ΢Σϒ࠷৽࢓༷Λ༻͍ͨߴ଎ͳܭࢉόοΫΤϯυ 8FC(16ɾ8FC(-ɾ8FC"TTFNCMZʹΑΔ$16(16྆ํͰͷߴ଎ͳ࣮ߦ  ܭࢉάϥϑ࠷దԽʹΑΔܭࢉͷߴ଎Խ ਪ࿦ϑΣʔζʹಛԽͨ͠࠷దԽʹΑΓܭࢉྔɾϞσϧύϥϝʔλαΠζΛ࡟ݮ  طଘͷਂ૚ֶशϑϨʔϜϫʔΫͷ޿ൣғͳαϙʔτ ,FSBT
    $BGGF
    $IBJOFS
    5FOTPS'MPXʹରԠ