| Budget Amount *help |
¥16,380,000 (Direct Cost: ¥12,600,000、Indirect Cost: ¥3,780,000)
Fiscal Year 2021: ¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥8,190,000 (Direct Cost: ¥6,300,000、Indirect Cost: ¥1,890,000)
Fiscal Year 2019: ¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
|
|---|
| Outline of Final Research Achievements |
With the widespread use of AI technology, ultra-low latency convolutional neural network (CNN) processing is highly demanded in fields that require real-time image classification such as autonomous driving and VR applications. This paper proposes an ultra-low-latency all-digital in-imager 2D binary convolutional neural network (II2D-BNN) accelerator for image classification. In II2D-BNN, multiply-accumulate operations (MACs) are processed inside the imager array parallelly in 2D, without extra latency for the row-by-row processing and data access with random access memories (RAMs). Convolution and sub-sampling operations using a 3 × 3 kernel are completed in only nine steps of batch-processing-in-2D regardless of image size using the II2D-BNN architecture, leading to over 88.5% reduction in computing latency compared with state-of-the-art architectures using batch-processing-in-1D.
|
