| Project/Area Number |
16K12494
|
|---|
| Research Category |
Grant-in-Aid for Challenging Exploratory Research
|
| Allocation Type | Multi-year Fund |
|---|
| Research Field |
Soft computing
|
|---|
| Research Institution | Tohoku University |
|---|
Principal Investigator |
ONIZAWA Naoya 東北大学, 電気通信研究所, 助教 (90551557)
|
|---|
| Project Period (FY) |
2016-04-01 – 2019-03-31
|
| Project Status |
Completed (Fiscal Year 2018)
|
| Budget Amount *help |
¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2018: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2017: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2016: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
|
|---|
| Keywords | ソフトコンピューティング / 確率的演算 / 情報システム |
|---|
| Outline of Final Research Achievements |
Recently, brain-inspired vision processing provides better accuracy than human begins in specific applications. However, the current brain-inspired computing works well in only one application previously trained, and hence is lack of flexibility in unknown various applications, unlike human beings.
In this study, a brain-inspired vision-processing filter is realized that can extract millions of different features, like human beings. In addition, as the hardware is implemented using stochastic computing that exhibits a similar behavior to human neurons, the power dissipation is reduced by 97% while maintaining the throughput in comparison with a traditional binary implementation.
|
| Academic Significance and Societal Importance of the Research Achievements |
本研究課題で実現した人間的視覚処理ハードウェアは，人間の脳のように幅広いパターン（1000万パターン以上）の特徴を抽出することが出来る．この特徴データを用いることで，将来的には汎用的な脳型処理・人工知能実現に繋がるものと考えられる．というのも，現在実現されている人工知能（一般的にニューラルネットワーク）は，特徴をタスクに応じて絞っているために，処理の汎用性が失われていると考えられる．つまり将来，汎用人工知能実現に向けて，本研究課題で実現したハードウェアは，その初期段階の処理に当たる部分が実現されたものと考えられ，学術的に重要な意義を有する．
|
