deep learning acceleratorsgraphics processing unitsneural processing unitsneuromorphic processorsDeep learning (DL) has proven to be one of the most pivotal components of machine learning given its notable performance in a variety of application domains. Neural networks (NNs) for DL are tailored to ...
Deep learning applications are exploding, especially those that use images for computer vision. We see these applications in everything from self-driving cars to significant advances in healthcare. However, AI-infused applications that need to b...
In this context, the primary issues faced by hardware accelerators are loss of accuracy and high power consumption. Introduction Recent progress in deep learning and convolutional neural networks (CNNs) has contributed to the advances in artificial intelligence with respect to tasks such as object ...
The proposed IPU is able to remove such bottlenecks by fully offloading these tasks to dedicated special-purpose accelerators. To show the advantages of the proposed solution, a set of experiments have been carried out testing the IPU in combination with the AMD Xilinx Deep Learning Processor Unit...
(MAC), with length 5–8μμm). The DONN thus scales favorably with respect to very large DNN accelerators: the DONN’s optical communication cost for an 8-bit MAC, i.e., the energy to transmit two 8-bit values, remains constant at∼3fJ/MAC, whereas multi-chiplet systems have much...
Applications of artificial intelligence (AI) necessitate AI hardware accelerators able to efficiently process data-intensive and computation-intensive AI workloads. AI accelerators require two types of memory: the weight memory that stores the parameters
The first group of CPU/GPU based accelerators offers highly flexible and easy to develop solutions, in terms of supported CNN architectures, kernel parameters, nonlinear activation functions, pooling algorithms, and deep learning software frameworks (Caffe, TensorFlow, Keras, Matlab), but is inefficient...
1, they may not meet the hardware requirement of machine learning for Real-world problems for specific applications. With the trend of performance evolution of GPU hardware accelerators over the years, real-world applications demand hardware accelerators with performance exceeding Moore's law. To ...
Compiling Deep Learning Models for Custom Hardware Accelerators Convolutional neural networks (CNNs) are the core of most state-of-the-art deep learning algorithms specialized for object detection and classification. CNNs are both computationally complex and embarrassingly parallel. Two properties that ...
Changes in algorithms, models, operators, or numerical systems threaten the viability of specialized hardware accelerators. We propose VTA, a programmable deep learning architecture template designed to be extensible in the face of evolving workloads. VTA achieves this flexibility via a parametrizable ...