Samsung Electronics Co., Ltd.

Ulsan National Institute of Science and Technology

Yesung Kang

Seokhyeong Kang

Sunghoon Kim

Yoonho Park

A neural network accelerator includes an operator that calculates a first operation result based on a first tiled input feature map and first tiled filter data, a quantizer that generates a quantization result by quantizing the first operation result based on a second bit width extended compared with a first bit width of the first tiled input feature map, a compressor that generates a partial sum by compressing the quantization result, and a decompressor that generates a second operation result by decompressing the partial sum, the operator calculates a third operation result based on a second tiled input feature map, second tiled filter data, and the second operation result, and an output feature map is generated based on the third operation result.

Neural network accelerator and operating method thereof

A finite impulse response (FIR) filter circuit design method using approximate computing, the FIR filter circuit design method including: replacing adders of the FIR filter with approximate adders; and performing a synthesis work according to a set approximate synthesis flow when the replacing of the adders of the FIR filter are replaced with the approximate adders is performed, wherein, in the approximate synthesis flow, a numeric column of each of the approximate adders is divided into an accurate part and an inaccurate part, and a numeric column of the inaccurate part is approximated. In the FIR filter, conventional adders/subtractors are replaced with addition/subtraction having an automated synthesis flow so that energy consumption can be reduced.

Growing community of inventors

Ulsan, South Korea

Yesung Kang