Rethinking Embedded Blocks for Machine Learning Applications

The underlying goal of FPGA architecture research is to devise flexible substrates that implement a wide variety of circuits efficiently. Contemporary FPGA architectures have been optimized to support networking, signal processing, and image processing applications through high-precision digital signal processing (DSP) blocks. The recent emergence of machine learning has created a new set of demands characterized by: (1) higher computational density and (2) low precision arithmetic requirements. With the goal of exploring this new design space in a methodical manner, we first propose a problem formulation involving computing nested loops over multiply-accumulate (MAC) operations, which covers many basic linear algebra primitives and standard deep neural network (DNN) kernels. A quantitative methodology for deriving efficient coarse-grained compute block architectures from benchmarks is then proposed together with a family of new embedded blocks, called MLBlocks. An MLBlock instance includes several multiply-accumulate units connected via a flexible routing, where each configuration performs a few parallel dot-products in a systolic array fashion. This architecture is parameterized with support for different data movements, reuse, and precisions, utilizing a columnar arrangement that is compatible with existing FPGA architectures. On synthetic benchmarks, we demonstrate that for 8-bit arithmetic, MLBlocks offer 6× improved performance over the commercial Xilinx DSP48E2 architecture with smaller area and delay; and for time-multiplexed 16-bit arithmetic, achieves 2× higher performance per area with the same area and frequency. All source codes and data, along with documents to reproduce all the results in this article, are available at http://github.com/raminrasoulinezhad/MLBlocks .

A Computational Look at Oral History Archives

Computational technologies have revolutionized the archival sciences field, prompting new approaches to process the extensive data in these collections. Automatic speech recognition and natural language processing create unique possibilities for analysis of oral history (OH) interviews, where otherwise the transcription and analysis of the full recording would be too time consuming. However, many oral historians note the loss of aural information when converting the speech into text, pointing out the relevance of subjective cues for a full understanding of the interviewee narrative. In this article, we explore various computational technologies for social signal processing and their potential application space in OH archives, as well as neighboring domains where qualitative studies is a frequently used method. We also highlight the latest developments in key technologies for multimedia archiving practices such as natural language processing and automatic speech recognition. We discuss the analysis of both visual (body language and facial expressions), and non-visual cues (paralinguistics, breathing, and heart rate), stating the specific challenges introduced by the characteristics of OH collections. We argue that applying social signal processing to OH archives will have a wider influence than solely OH practices, bringing benefits for various fields from humanities to computer sciences, as well as to archival sciences. Looking at human emotions and somatic reactions on extensive interview collections would give scholars from multiple fields the opportunity to focus on feelings, mood, culture, and subjective experiences expressed in these interviews on a larger scale.

Emotion Recognition Robust to Indoor Environmental Distortions and Non-targeted Emotions Using Out-of-distribution Detection

The rapid development of machine learning on acoustic signal processing has resulted in many solutions for detecting emotions from speech. Early works were developed for clean and acted speech and for a fixed set of emotions. Importantly, the datasets and solutions assumed that a person only exhibited one of these emotions. More recent work has continually been adding realism to emotion detection by considering issues such as reverberation, de-amplification, and background noise, but often considering one dataset at a time, and also assuming all emotions are accounted for in the model. We significantly improve realistic considerations for emotion detection by (i) more comprehensively assessing different situations by combining the five common publicly available datasets as one and enhancing the new dataset with data augmentation that considers reverberation and de-amplification, (ii) incorporating 11 typical home noises into the acoustics, and (iii) considering that in real situations a person may be exhibiting many emotions that are not currently of interest and they should not have to fit into a pre-fixed category nor be improperly labeled. Our novel solution combines CNN with out-of-data distribution detection. Our solution increases the situations where emotions can be effectively detected and outperforms a state-of-the-art baseline.