Deep learning algorithms achieve high classification accuracy at the expense of significant
computation cost. To address this cost, a number of quantization schemes have been …

Deep learning algorithms achieve high classification accuracy at the expense of significant
computation cost. In order to reduce this cost, several quantization schemes have gained …

A multi-TOPS AI core is presented for acceleration of deep learning training and inference in
systems from edge devices to data centers. With a programmable architecture and custom …

Deep learning algorithms achieve high classification accuracy at the expense of significant
computation cost. In order to reduce this cost, several quantization schemes have gained …

Deep Neural Networks (DNNs) represent the state-of-the-art in many Artificial Intelligence (AI)
tasks involving images, videos, text, and natural language. Their ubiquitous adoption is …

Increasing transistor counts in modern processors can create instantaneous changes in
current, driving nanosecond-speed supply voltage (V DD ) droops that require extra guardband …

A constant delay (CD) logic style is proposed in this paper, targeting at full-custom high-speed
applications. The CD characteristic of this logic style regardless of the logic type makes it …

We introduce Lumos, the first end-to-end multimodal question-answering system with text
understanding capabilities. At the core of Lumos is a Scene Text Recognition (STR) …

Weight quantization for deep ConvNets has shown promising results for applications such
as image classification and semantic segmentation and is especially important for …

An effective recipe for building seq2seq, non-autoregressive, task-oriented parsers to map
utterances to semantic frames proceeds in three steps: encoding an utterance $x$, predicting …