lecture 4

Deep neural networks (DNN), Convolutional neural networks (CNN), Recurrent neural networks (RNN)

Supervised learning is based on labeled data sets, while unsupervised learning is based on no data labels.

Recurrent neural networks, including long short-term memory (LSTM), and gated recurrent units (GRU) are used for semi-supervised learning.

<ol> <li>Choosing the right deep learning network. 2. Handling billions of multiply-accumulate operations and large parameter data. 3. Dealing with continuous stream of new algorithms.</li> </ol> Signup and view all the answers

<p>Caffe, TensorFlow, Mxnet, Darknet, Keras, PyTorch</p> Signup and view all the answers

<ol> <li>VGGNet, GoogleNet, ResNet for classification tasks. 2. SSD, YOLO, Faster R-CNN for detection tasks. 3. Semantic Segmentation and Instance Segmentation for segmentation tasks.</li> </ol> Signup and view all the answers

<ol> <li>Computational intensive operations. 2. Memory bandwidth intensive. 3. Power efficiency and deployment.</li> </ol> Signup and view all the answers

<p>Pruning, fine-grained pruning, and coarse-grained pruning.</p> Signup and view all the answers

<p>To gradually reduce the number of model parameters while minimizing accuracy loss.</p> Signup and view all the answers

<p>Significantly reducing the operations and parameters of CNN models without losing much accuracy.</p> Signup and view all the answers

<ol> <li>Analyze the original baseline model. 2. Prune the input model. 3. Finetune the pruned model. 4. Repeat steps 2 and 3 several times. 5. Transform the pruned sparse model to the final dense model.</li> </ol> Signup and view all the answers

<p>Quantization allows for the use of integer computing units and representation of weights and activations by lower bits, reducing computing complexity. Channel pruning reduces the overall required operations. Both techniques help achieve high performance and energy efficiency with minimal degradation in accuracy.</p> Signup and view all the answers

<p>The conversion to 8-bit integer (INT8) format reduces computing complexity without losing prediction accuracy. It also enables the fixed-point network model to require less memory bandwidth, resulting in faster speed and higher power efficiency compared to the floating-point model.</p> Signup and view all the answers

<p>The AI quantizer supports common layers in neural networks, including but not limited to convolution, pooling, fully connected, and batchnorm.</p> Signup and view all the answers

<p>Two challenges in developing deep learning solutions are computational intensity and memory bandwidth intensity.</p> Signup and view all the answers

<p>The purpose of pruning in deep neural networks is to eliminate redundant weights while minimizing accuracy loss.</p> Signup and view all the answers

<p>The role of the AI optimizer in deep learning is to prune redundant connections and reduce the overall required operations.</p> Signup and view all the answers

<p>The key features of the DPU Alveo U200/U250 Cards include throughput oriented and high-efficiency computing engines, wide range of convolutional neural network support, compressed convolutional neural networks, and optimization for high-resolution image optimization.</p> Signup and view all the answers

<p>The DPUCZDX8G is designed to be integrated as a block in the programmable logic (PL) of Zynq-7000 SoCs and Zynq UltraScale+ MPSoCs, with direct connections to the processing system (PS). It is user configurable and exposes several parameters, and is optimized for efficiency, low latency, and scalability.</p> Signup and view all the answers

<p>The DPUCZDX8G hardware architecture consists of a configurable and extensible architecture that provides multi-dimension parallelism. It includes computing engines for major convolution calculations, a configuration module with encoders and decoders to squeeze the network model size, and a data controller module that schedules the data flow in the DPU.</p> Signup and view all the answers

<p>The three stages of the DPUCZDX8G IP Core data flow are image pre-processing, compute (accelerating network graph elements in the PL using the IP core), and image post-processing, which varies based on the network goal and topology.</p> Signup and view all the answers

<p>The purpose of the Deep Learning Processor Unit (DPU) is to serve as a programmable engine optimized for deep neural networks. It is designed to accelerate the computation of neural network models and is widely used in various computer vision applications such as image or video classification, semantic segmentation, object detection, and tracking.</p> Signup and view all the answers

<p>The Alveo U50/U280 DPU has the following key features: a high-performance scheduler, a hybrid computing array module, an instruction fetch unit module, and a global memory pool module. It uses a specialized instruction set that allows efficient implementation of many convolutional neural networks, such as VGG, ResNet, GoogLeNet, YOLO, SSD, MobileNet, and FPN. It is optimized for high-throughput applications.</p> Signup and view all the answers

<p>The DPUCZDX8G IP can be integrated as a block in the programmable logic of Zynq UltraScale+ MPSoCs. It is user-configurable and exposes several parameters to optimize resources or support different features. It is designed to be efficient, have low latency, and supports the most commonly used network layers and operators, using hardware acceleration. It takes full advantage of the underlying Xilinx FPGA architecture and achieves the optimal tradeoff between latency, power, and cost.</p> Signup and view all the answers

<p>The AI quantizer reduces computing complexity by converting the 32-bit floating-point weights and activations used in training neural networks to 8-bit integer (INT8) format. This fixed-point network model requires less memory bandwidth, resulting in faster speed and higher power efficiency compared to the floating-point model. The quantization process preserves the prediction accuracy of the network model while reducing the computational requirements, making it more efficient for deployment on hardware platforms.</p> Signup and view all the answers