Unverified50% confidenceFactExact time
Single-precision floating-point (float, 32-bit) has only 23 mantissa bits, and can precisely represent integers in the range of approximately ±2^24.
1
Sources
50%
Confidence
Long-term
Relevance
8/19/2026
First Seen
Sources
整数除法转浮点除法:反直觉的CPU性能优化技巧详解
Related Claims
Unverifiedfloat32的有效精度约为7位十进制数字,float64约为15位70% similarUnverifiedFP32 uses 32 binary bits to represent a number (1 sign bit, 8 exponent bits, 23 mantissa bits) and can accurately represent approximately 7 significant decimal digits.69% similarUnverifiedFor double-precision floating-point numbers, one ULP near the value 1.0 is approximately 2^-52 ≈ 2.22×10^-16.68% similarUnverifiedThe model ultimately outputs a tensor of 201×18×4, where 4 represents 4 lane lines, 18 represents key rows, and 201 represents 200 grid positions plus 1 non-existence class.68% similarUnverifiedA double-precision floating-point number can accurately represent approximately 15–17 significant decimal digits.67% similar
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