quantizing deep convolutional networks for efficient inference: a whitepaper
quantizing deep convolutional networks for efficient inference: a whitepaper
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quantizing deep convolutional networks for efficient inference: a whitepaper trader joe's birria calories
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quantizing deep convolutional networks for efficient inference: a whitepaper
EIE: efficient inference engine on compressed deep neural network, M.Isard, Y.Jia, R.Jozefowicz, L.Kaiser, M.Kudlur, J.Levenberg, Deep networks are increasingly used for applications at the edge. tf.__version __ 2016. This can be further improved by noting that the weights are constant at inference and by noting that the sum over activations is identical for all convolutional kernels of the same size. We also modify the bias terms correspondingly. In our experiments, it was better to use a ReLU than a ReLU6 for the activations. In this section, we describe how quantization is modeled during training and describe how this can be easily done using automatic quantization tools in TensorFlow. precision post-training produces classification accuracies within 2 View 3 excerpts, references methods and background. We note that fine tuning improves accuracy in this case also. Quantizing without corrections shows high jitter (green curve). This work is exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization. Fixed-point Quantization of Convolutional Neural Networks for Quantized This work presents F8Net, a novel quantization framework consisting of only fixed-point 8-bit multiplication, which achieves comparable and better performance, when compared not only to existing quantization techniques with INT32 multiplication or floating-point arithmetic, but also to the full-precision counterparts, achieving state-of-the-art performance. round A quantization scheme is proposed that allows inference to be carried out using integer- only arithmetic, which can be implemented more efficiently than floating point inference on commonly available integer-only hardware. We do not consider stochastic quantization for inference as most inference hardware does not support it. Table 1 shows the wide variation in model size and accuracy across these networks. tf.contrib.quantize.create_eval_graph(). The total number of kernels is 8. It is also possible to perform quantization aware training for improved accuracy, Deep Convolutional networks: Model size and accuracy. Having this flexibility can further reduce model size and processing time. Having lower precision weights and activations allows for better cache reuse. Quantizing deep convolutional networks for efficient inference: A whitepaper. Networks for Mobile Vision Applications, Apr. 0 Note that this can cause a loss of precision in the case of extreme one-sided distributions. Introduction2. networks, 2016. Therefore, there is a pressing need for techniques to optimize models for reduced model size, faster inference and lower power consumption. Cross-Lay, , Post Training Quantization() Quantization Aware Training(), (checkpoint). Convolutional Neural Networks (CNNs) have proven to be a powerful state-of-the-art method for image classification tasks. Post training quantization of weights and activations: per-channel quantization of weights and per-layer quantization of activations works well for all the networks considered, with asymmetric quantization providing slightly better accuracies. This work is exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization. Rocky Rhodes provided the performance measurement numbers for the models. = https://github.com/google/gemmlowp/blob/master/doc/quantization.md#implementation-of-quantized-matrix-multiplication. https://arxiv.org/pdf/1806.08342.pdf, We experiment with several configurations for training quantized models: Use Exponential moving averaging for quantization with caution. quantization-aware training also allows for reducing the precision of weights to four bits with accuracy losses ranging from 2% to 10%, with higher accuracy drop for smaller networks.we introduce tools in tensorflow and tensorflowlite for quantizing convolutional networks and review best practices for quantization-aware training to obtain high 1 One does not need to develop a new model architecture for improved speed. Mobilenet_v1_1_224: Comparison of Batch normalization quantization schemes: Batch normalization without corrections (green) shows a lot of jitter due to the changing scaling of weights from batch to batch. Inference time measurements on Pixel2 phone in milliseconds on a single large core. We present an overview of techniques for quantizing convolutional neural networks for inference with integer weights and activations. One drawback however is the high computational complexity and high memory consumption of CNNs which makes them unfeasible for execution on embedded platforms which are constrained on physical resources needed to support CNNs. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. To match this, fake quantization operations should not be placed between the addition and the ReLU operations. Since we use quantized weights and activations during the back-propagation, the floating point weights converge to the quantization decision boundaries. This allows for the network to learn weight values to better compensate for the deterministic distortion introduced by weight quantization. Inference on Fixed-Point Hardware, EasyQuant: Post-training Quantization via Scale Optimization, U-Net Fixed-Point Quantization for Medical Image Segmentation, Quantization of Deep Neural Networks for Accumulator-constrained We measure the run-times (Table 7) on a single large core of the Google Pixel 2 device for both floating point and quantized models. X One can do better by using the equation above and noting that the last term is a constant and each of the other terms requires N multiplies, which is 3x more operations than the 8-bit dot product. After sufficient training, switch from using batch statistics to long term moving averages for batch normalization, using the optional parameter freeze_bn_delay in. 2017. can be reduced by a factor of 4 by quantizing weights to 8-bits, even when K.He, X.Zhang, S.Ren, and J. Comparison of post training weight and activation quantization schemes:Mobilenet-v1. to improve low-precision network accuracy, 2017. Asymmetric, per-layer (Post Training Quantization), Symmetric , per-channel (Post Training Quantization), Asymmetric, per-layer (Quantization Aware Training), Symmetric, per-channel (Quantization Aware Training), Symmetric,per-channel (Post Training Quantization). M.Abadi, A.Agarwal, P.Barham, E.Brevdo, Z.Chen, C.Citro, G.S. Corrado, Model sizes can be reduced by a . There is a clear tradeoff between model size and compressibility. In order to better understand the benefits of quantization aware training, we perform experiments to assess performance at 4 bit quantization for weights and activations. ] In the second experiment, we compare naive batch norm folding and batch normalization with correction and freezing for Mobilenet_v2_1_224. We also show that at 4 bit precision, quantization aware training provides significant improvements over post training quantization schemes. Less working memory and cache for activations: Intermediate computations are typically stored in cache for reuse by later layers of a deep network and reducing the precision at which this data is stored leads to less working memory needed. C.Szegedy, V.Vanhoucke, S.Ioffe, J.Shlens, and Z.Wojna, A simple one-line change to the training or evaluation code automatically inserts simulated quantization operations into the training or eval graph. a Correction with freezing show good accuracy (blue and red curves). m Batch renormalization (red) improves the jitter, but does not eliminate it. losses ranging from 2 In this case, one can fuse the addition and the ReLU operation at inference time in most platforms. This can make trivial operations like addition, figure 6 and concatenation , figure 7 non-trivial due to the need to rescale the fixed point values so that addition/concatenation can occur correctly. For evaluating the tradeoffs with different quantization schemes, we study the following popular networks and evaluate the top-1 classification accuracy. The total number of kernels is 128. , Histogram of the SQNR per output feature map (in dB on the x-axis), showing the number of kernels for each SQNR bin for different weight quantization schemes for layer:Conv2d_1_depthwise, Mobilenet_v1_0.25_128. systems, 2015. Neural Network Inference, Trained Uniform Quantization for Accurate and Efficient Neural Network Quantization aware training can substantially improve the accuracy of models by modeling quantized weights and activations during the training process. For weights, we use the actual minimum and maximum values to determine the quantizer parameters. A White Paper on Neural Network QuantizationQuantizing deep convolutional networks for efficient inference: A whitepaper18 . M.Andreetto, and H.Adam, MobileNets: Efficient Convolutional Neural For an indepth discussion, please see [16]. Approximation for purposes of derivative calculation (bottom). View 7 excerpts, cites methods and background, 2019 Fifth Workshop on Energy Efficient Machine Learning and Cognitive Computing - NeurIPS Edition (EMC2-NIPS). This is important to ensure that common operations like zero padding do not cause quantization error. A learned linear symmetric quantizer for integer neural network processors is proposed, which not only quantizes neural parameters and activations to low-bit integer but also accelerates hardware inference by using batch normalization fusion and low-precision accumulators and multipliers. We first show results for Mobilenetv1 networks and then tabulate results across a broader range of networks. 2022 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. View 2 excerpts, cites background and methods, View 6 excerpts, cites background and methods, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Approximation for purposes of derivative calculation (bottom). https://www.qualcomm.com/news/onq/2018/02/01/how-can-snapdragon-845s-new-ai-boost-your-smartphones-iq. detection and segmentation, 2018. Model sizes can be reduced by a factor of 4 by quantizing weights to 8-bits, even when 8-bit arithmetic is not supported. Per-channel quantization: Support for per-channel quantization of weights is critical to allow for: Easier deployment of models in hardware, requiring no hardware specific fine tuning. Note under performance of EMA weights (red curve) after sufficient training. In many deep architectures, memory access can dominate power consumption [2]. The backward pass is modeled as a straight through estimator (see. training quantization of weights.We benchmark latencies of quantized networks Per-channel quantization of weights and per-layer quantization of activations to 8-bits of precision post-training produces classification accuracies within 2% of floating point networks for a wide variety of CNN architectures. It is critical to match quantized inference with the forward pass of training. A.K. Mishra, E.Nurvitadhi, J.J. Cook, and D.Marr, WRPN: wide Exponential Moving Averages of weights may under-perform instantaneous estimates during quantization aware training and must be used with caution. The scale specifies the step size of the quantizer and floating point zero maps to zero-point [4]. One can obtain further acceleration by supporting a range of precisions for arithmetic. (weights and activations), Convert model: The savedmodel with range information is transformed into a flatbuffer file using the tensorflow converter (TOCO) at: tf.contrib.lite.toco_convert. During the initial phase of training, we undo the scaling of the weights so that outputs are identical to regular batch normalization. Note that activations are quantized to 8-bits in these experiments. This paper presents a comparison of model-parameter driven quantization approaches that can achieve as low as 3-bit precision without affecting accuracy and shows the methods to lower bit-precision beyond quantization limits with object class clustering. https://intel.github.io/mkl-dnn/index.html. We also compare the post training quantization accuracies of popular convolutional networks: Inception-V3, Mobilenet-V2, Resnet-v1-50, Resnet-v1-152, Resnet-v2-50, Resnet-v2-152 and Nasnet-mobile on ImageNet in figure 4. ReLU6: Used in Mobilenet-V1, which restricts the activations to be in a fixed range (0,6) for all feature maps, thereby removing large dynamic range variations. In many cases, one can start with an existing floating point model and quickly quantize it to obtain a fixed point quantized model with almost no accuracy loss, without needing to re-train the model. Multiple hardware platforms and libraries support fast inference with quantized weights and activations, so there is no need to wait for new hardware development. architectures for scalable image recognition, 2017. Based on our experiments, we make the following conclusions: Use symmetric-per-channel quantization of weights with post training quantization as a starting point. This also leads to faster download times for model updates. Activations can be quantized to 8-bits with almost no loss in accuracy. In this section, we describe several strategies for quantizing batch normalization layers. Therefore, this function is well behaved for purposes of calculating gradients. Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI) applications including computer vision, speech recognition, and robotics. Quantized inference at 8-bits can provide 2x-3x speed-up on a CPU and close to 10x speedup compared to floating point inference on specialized processors optimized for low precision wide vector arithmetic, like the Qualcomm DSP with HVX. on CPUs and DSPs and observe a speedup of 2x-3x for quantized implementations Once the scale and zero-point are dened, quantization proceeds . Zero-point is an integer, ensuring that zero is quantized with no error. Weight quantization at the granularity of a layer causes large accuracy drops primarily due to batch normalization, which causes extreme variation in dynamic range across convolution kernels in a single layer. training by reducing internal covariate shift, 2015. import tensorflow as tf This work analyzed the root cause of quantization loss and proposed a quantization-friendly separable convolution architecture that can archive 8-bit inference top-1 accuracy and almost closed the gap to the float pipeline. , 2021 googlePTQ We also evaluate the accuracies obtained for different quantization schemes with quantization aware training and show that even per-layer quantization schemes show high accuracies post training at 8-bits of precision. Model sizes Matching Batch normalization with inference reduces jitter and improves accuracy. Note that we use simulated quantized weights and activations for both forward and backward pass calculations. We show that per-channel quantization provides big gains over per-layer quantization for all networks. We present an overview of techniques for quantizing convolutional neural networks for inference with integer weights and activations. m We present an overview of techniques for quantizing convolutional neural networks for inference with integer weights and activations. We compare the accuracies obtained with 4 bit per-channel quantization of weights with 8-bit quantization across different depth multipliers in figure 17. Note t This is consistent with the general observation that it is better to train a model with more degrees of freedom and then use that as a teacher to produce a smaller model (. Per-channel quantization of weights and per-layer quantization of activations to 8-bits of precision post-training produces classification accuracies within 2% of floating point networks for a wide variety of CNN architectures. This step creates a flatbuffer file that converts the weights into integers and also contains information for quantized arithmetic with activations. Execute model: The converted model with integer weights can now be executed using the TFLite interpreter which can optionally execute the model in custom accelerators using the NN-API. We discuss multiple approaches for model quantization and show the performance impact for each of these approaches. . From figure 2, we note that per-channel quantization is required to ensure that the accuracy drop due to quantization is small, with asymmetric, per-layer quantization providing the best accuracy. Quantizing deep convolutional networks for efficient inference: A whitepapergoogle. We hypothesize that quantizing activations introduces random errors as the activation patterns vary from image to image, while weight quantization is deterministic. [X_{min},X_{max}], [ We repeat the same experiments for quantized weights and activations with training, starting from a floating point check-point and with batch normalization freezing and obtain the results shown in figures 11 and 10 and Table 4. Weight Power Distribution: We also plot the distribution of the sample weights, normalized by the average power, i.e we plot. It is also necessary to reduce the amount of communication to the cloud for transferring models to the device to save on power and reduce network connectivity requirements. Dean, Distilling the Knowledge in a i [0,N_{l-1}] This paper formulates quantization bitwidth as a hyperparameter in the optimization problem of selecting the bitwidth by leveraging a state-of-the-art policy gradient based Reinforcement Learning (RL) algorithm called Proximal Policy Optimization [10] (PPO), to efficiently explore a large design space of DNN quantization. training to obtain high accuracy with quantized weights and activations. Optionally fine tune if there is an accuracy drop. An energy efficient inference engine (EIE) that performs inference on this compressed network model and accelerates the resulting sparse matrix-vector multiplication with weight sharing and is 189x and 13x faster when compared to CPU and GPU implementations of the same DNN without compression. M.Courbariaux, Y.Bengio, and J.David, Binaryconnect: Training deep neural Our recommendation is to support 4,8 and 16-bit weights and activations. This paper proposes two new compression methods, which jointly leverage weight quantization and distillation of larger teacher networks into smaller student networks, and shows that quantized shallow students can reach similar accuracy levels to full-precision teacher models. One can also run the model on the CPU. For inference, we fold the batch normalization into the weights as defined by equations 20 and 21. In section 4 and show that batch normalization with correction and freezing provides the best accuracy. Networks with more parameters like Resnets and Inception-v3 are more robust to quantization compared to Mobilenets which have fewer parameters. network with pruning, trained quantization and huffman coding,, GEMMLOWP, Gemmlowp: a small self-contained low-precision GEMM library., Intel(R) MKL-DNN, Intel(R) Math Kernel Library for Deep Neural Faster computation: Most processors allow for faster processing of 8-bit data. Quantizing a model can provide multiple benefits as discussed in section 1. Background & motivationPrevious system, slimslim Stochastic Quantization does not improve accuracy: Comparison of stochastic quantization vs deterministic quantization during training. Our first experiment compares stochastic quantization with deterministic quantization. Tensorflowtflite recommend that per-channel quantization of weights and per-layer quantization In addition, it is important to ensure that fusion of operations at inference time is modeled correctly during training. z Note that this comparison allows us to evaluate a depth vs quantization tradeoff (see [31]). Consider a floating point variable with range (xmin,xmax) that needs to be quantized to the range (0,Nlevels1) where Nlevels=256 for 8-bits of precision. <1mb model size,. 2015. Inference on Embedded Platforms, Iterative Training: Finding Binary Weight Deep Neural Networks with Lower Power: Moving 8-bit data is 4 times more efficient than moving 32-bit floating point data. (Neil deGrasse Tyson) http://arm-software.github.io/CMSIS_5/NN/html/index.html. residuals and linear bottlenecks: Mobile networks for classification, networks.We introduce tools in TensorFlow and TensorFlowLite for quantizing The quantizer parameters can be determined using several criteria. Quantization aware training models quantization during training and can provide higher accuracies than post quantization training schemes. H.Adam, and D.Kalenichenko, Quantization and Training of Neural Freezing the moving mean and variance updates after step 200000 allows for quantized weights to adapt to the batch norm induced scaling and provides the best accuracy with minimal jitter (blue curve). Develop Faster Deep Learning Frameworks and Applications. While DNNs deliver, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). [0,N_{l-1}], z Consider a 2D convolution between a weight and an activation: A naive implementation of convolution, by performing the addition of zero-point prior to the convolution, leads to a 2x to 4x reduction in the throughput due to wider (16/32-bit) operands. Note that the quantization granularity is different for activations and weights, so this is not a fair comparison of the impact of quantization. X There is a large drop when weights are quantized at the granularity of a layer, particularly for Mobilenet architectures. (See figure 2) Typically, about 100 mini-batches are sufficient for the estimates of the ranges of the activation to converge. Quantizing a model from a floating point checkpoint provides better accuracy: Fine tuning a floating point checkpoint provides better fixed point accuracy. For SGD, the updates are given by: Quantization aware training is achieved by automatically inserting simulated quantization operations in the graph at both training and inference times using the quantization library at [23] for Tensorflow [24]. This paper proposes two new compression methods, which jointly leverage weight quantization and distillation of larger teacher networks into smaller student networks, and shows that quantized shallow students can reach similar accuracy levels to full-precision teacher models. Quantization aware training closes the gap to floating point accuracy, even for per-layer quantization of weights. While 4 and 8-bit precisions are sufficient for classification, higher precision support is likely needed for regression applications, like super-resolution and HDR image processing. Quantization-aware training can provide further improvements, reducing the (green curve in 14 and 15) cnn x We note that at 8-bits of precision, post training quantization schemes provide close to floating point accuracy. Per-channel quantization side-steps this problem by quantizing at the granularity of a kernel, which makes the accuracy of per-channel quantization independent of the batch-norm scaling. floating point networks for a wide variety of CNN architectures. Therefore, at inference there is no explicit batch normalization. This can be. A quantization scheme is proposed that allows inference to be carried out using integer- only arithmetic, which can be implemented more efficiently than floating point inference on commonly available integer-only hardware. Note that other approaches like weight regularization can also improve the accuracy of quantization post training, please see [22]. The losses due to activation quantization are more severe than that of weight quantization (see Table 6). z=0, Going forward, we plan to enhance our automated quantization tool to enable better quantization of networks by investigating the following areas: Regularization techniques to better control the dynamic ranges of weights and activations can provide further improvements.
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