to cast to FP16. FP16 operations require 2X reduced memory bandwidth (resulting in a 2X speedup for bandwidth-bound operations like most pointwise ops) and 2X reduced memory storage for intermediates (reducing to cast to FP16. FP16 operations require 2X reduced memory bandwidth (resulting in a 2X speedup for bandwidth-bound operations like most pointwise ops) and 2X reduced memory storage for intermediates (reducing to cast to FP16. FP16 operations require 2X reduced memory bandwidth (resulting in a 2X speedup for bandwidth-bound operations like most pointwise ops) and 2X reduced memory storage for intermediates (reducing

and achieves an 80% accuracy with the same number of training steps and labels. Thus, delivering a 2x model compression. Again, this is a hypothetical scenario which illustrates how learning techniques straightforward to apply them on any dataset. A single transformation on every sample results in a dataset 2x the original size. Two transformations applied separately result in a dataset 3x the original size on the pixel values. Let’s take brightness transformation as an example. Figure 3-6 shows an image 2x bright (bottom-right corner) as compared to the original image (center). This transformation causes

11 Percona Server 5.7.10‐3 Storage SAS HDD 2x SEAGATE ST600MP0005 15K rpm SATA SSD 2x Samsung 850 PRO NVMe SSD 2x Samsung XS1715 Quad‐socket (28 Core) Configuration Percona Server 5.7.11‐4 Storage SAS HDD 2x SEAGATE ST600MP0005 15K rpm SATA SSD 2x Samsung 850 Pro SAS SSD 2x Samsung PM1633 NVMe 2x Samsung PM1725 It is generally accepted that

runtime overhead (performance impact: depending on tool, from 2x up to 10x) extra-memory usage (for memory related tools/instrumentation), 2x or more sometimes difficult to map error reports into source runtime overhead (performance impact: depending on tool, from 2x up to 10x) extra-memory usage (for memory related tools/instrumentation), 2x or more sometimes difficult to map error reports into source Sanitizer (ASan) Very fast instrumentation
 The average slowdown of the instrumented program is ~2x github.com/google/sanitizers/wiki/AddressSanitizerPerformanceNumbers97 2020 Victor Ciura | @ciura_victor

1x 2x ix N x . . . . . . f y ? = ? ෍ ?=1 ? ???? + ? 6 1.人工神经网络发展历史 1982年，加州理工学院J.J.Hopfield 教授提出了Hopfield神经网络模型 ，引入了计算能量概念，给出了网 络稳定性判断。 离散Hopfield神经网络模型 1T 2T IT N T … … 1x 2x ix nx 线性分类模型。 用 ? ∈ ??×? 表示数据集，用 ? 表示标 签。 需要学习的目标函数是 从一堆输入输出中学习模型参数?和?。  1 w b 2 w iw N w 1x 2x ix N x . . . . . . f y 输入 权重 偏置 求和 求和 输出 ?(?) = sign(?T? + ?) 11 2.感知机算法 感知机算法（Perceptron

= 3 3 julia> 2x^2 - 3x + 1 10 julia> 1.5x^2 - .5x + 1 13.0 It also makes writing exponential functions more elegant: CHAPTER 4. INTEGERS AND FLOATING-POINT NUMBERS 20 julia> 2^2x 64 The precedence literal coefficients is slightly lower than that of unary operators such as negation. So -2x is parsed as (-2) * x and √2x is parsed as (√2) * x. However, numeric literal coefficients parse similarly to unary unary operators when combined with exponentiation. For example 2^3x is parsed as 2^(3x), and 2x^3 is parsed as 2*(x^3). Numeric literals also work as coefficients to parenthesized expressions: julia>

= 3 3 julia> 2x^2 - 3x + 1 10 julia> 1.5x^2 - .5x + 1 13.0 It also makes writing exponential functions more elegant: CHAPTER 4. INTEGERS AND FLOATING-POINT NUMBERS 20 julia> 2^2x 64 The precedence literal coefficients is slightly lower than that of unary operators such as negation. So -2x is parsed as (-2) * x and √2x is parsed as (√2) * x. However, numeric literal coefficients parse similarly to unary unary operators when combined with exponentiation. For example 2^3x is parsed as 2^(3x), and 2x^3 is parsed as 2*(x^3). Numeric literals also work as coefficients to parenthesized expressions: julia>

= 3 3 julia> 2x^2 - 3x + 1 10 julia> 1.5x^2 - .5x + 1 13.0 It also makes writing exponential functions more elegant: CHAPTER 4. INTEGERS AND FLOATING-POINT NUMBERS 20 julia> 2^2x 64 The precedence literal coefficients is slightly lower than that of unary operators such as negation. So -2x is parsed as (-2) * x and √2x is parsed as (√2) * x. However, numeric literal coefficients parse similarly to unary unary operators when combined with exponentiation. For example 2^3x is parsed as 2^(3x), and 2x^3 is parsed as 2*(x^3). Numeric literals also work as coefficients to parenthesized expressions: julia>

= 3 3 julia> 2x^2 - 3x + 1 10 julia> 1.5x^2 - .5x + 1 13.0 It also makes writing exponential functions more elegant: 22 CHAPTER 4. INTEGERS AND FLOATING-POINT NUMBERS julia> 2^2x 64 The precedence literal coefficients is slightly lower than that of unary operators such as negation. So -2x is parsed as (-2) * x and √2x is parsed as (√2) * x. However, numeric literal coefficients parse similarly to unary unary operators when combined with exponentiation. For example 2^3x is parsed as 2^(3x), and 2x^3 is parsed as 2*(x^3). Numeric literals also work as coefficients to parenthesized expressions: julia>