The 8-bit floating-point (FP8) format has gained growing interest in neural networks (NNs) for its superior dynamic range over traditional INT8. However, multiply-accumulate (MAC) operations remain a major source of power consumption during inference of NNs, which makes DSP-free design important, especially for edge FPGAs with few or no DSPs. Therefore, this brief presents FPGA-based FP8 approximate neural network engine (FANE), an FPGA-based approximate NN engine for FP8. We first introduce a novel approximation method that replaces the multiplications by linear additions. This approximate method reduces power consumption while maintaining high accuracy, outperforming the latest FP8 approximate multiplier by 53.15%. Based on this design, we construct an FP8 MAC unit and integrate it into both a convolution engine and a matrix–vector multiplication (MVM) unit. Finally, we integrate our design into a large language model (LLM). The result shows 61.5% higher efficiency (TOPS/W) than the previous design, demonstrating the superiority of FANE in terms of performance and power efficiency. The code of FANE is available on ourhttps://github.com/hanbao04/FANE-FPGA-based-FP8-Approximate-Neural-Network-Engine.git

Complex spectrum and magnitude are considered as two major features of speech enhancement and dereverberation. Traditional approaches always treat these two features separately, ignoring their underlying relationship. In this paper, we propose Uformer, a Unet based dilated complex & real dual-path conformer network in both complex and magnitude domain for simultaneous speech enhancement and dereverberation. We exploit time attention (TA) and dilated convolution (DC) to leverage local and global contextual information and frequency attention (FA) to model dimensional information. These three sub-modules contained in the proposed dilated complex & real dual-path conformer module effectively improve the speech enhancement and dereverberation performance. Furthermore, hybrid encoder and decoder are adopted to simultaneously model the complex spectrum and magnitude and promote the information interaction between two domains. Encoder decoder attention is also applied to enhance the interaction between encoder and decoder. Our experimental results outperform all SOTA time and complex domain models objectively and subjectively. Specifically, Uformer reaches 3.6032 DNSMOS on the blind test set of Interspeech 2021 DNS Challenge, which outperforms all top-performed models. We also carry out ablation experiments to tease apart all proposed submodules that are most important.

This paper described the PCG-AIID system for L3DAS22 challenge in Task 1: 3D speech enhancement in office reverberant environment. We proposed a two-stage framework to address multi-channel speech denoising and dereverberation. In the first stage, a multiple input and multiple out-put (MIMO) network is applied to remove background noise while maintaining the spatial characteristics of multi-channel signals. In the second stage, a multiple input and single out-put (MISO) network is applied to enhance the speech from desired direction and post-filtering. As a result, our system ranked 3rd place in ICASSP2022 L3DAS22 challenge and significantly outperforms the baseline system, while achieving 3.2% WER and 0.972 STOI on the blind test-set.

Research on single channel speech enhancement (SE) has a long tradition, but two main practical problems still remain unsolved. Firstly, it’s hard to balance between enhancement quality and computational efficiency, and low-latency always brings loss of quality. Secondly, enhancement in specific scenarios, such as singing and emotional speech, is also an intricate problem of conventional methods. In this paper, we propose a computationally efficient real-time speech enhancement network with densely connected multi-stage structures, which progressively enhances the channel-wise subband speech. The enhanced speech from earlier stage is used to guide the processing of deeper stage in order to obtain coarse to fine estimations. Besides, supervision is applied to all intermediate results in order to stabilize training and accelerate convergence. Moreover, an adaptive fine-tune step is utilized with some small datasets of specific scenarios, which achieves superb improvement under corresponding scenes. As a result, the proposed method achieves promising performance improvements in terms of speech quality and demonstrates robustness in complex scenarios. We submitt the proposed method to the deep noise suppression (DNS) challenge 2021, real-time denoising track, which was held by Microsoft. In the subjective evaluation, our system outperforms DNS-Challenge baseline by 0.14 points in terms of mean opinion score (MOS).

With the development of e-commerce platforms, e-commerce recommendation systems are playing an increasingly important role for the purpose of product recommendation. As a new attack model against e-commerce recommendation systems, the "Ride Item's Coattails" attack creates fake click information to establish the deceptive correlation between popular products and low-quality products in order to mislead the recommendation system of e-commerce platform to boost the sales of low-quality products. This attack is characterized by high concealment and strong destructiveness, which can cause great damage to e-commerce recommendation systems, and adversely affect the usability of the e-commerce platform and users' shopping experience. It is therefore of great practical significance to study how to quickly and effectively identify the false click information and the corresponding "Ride Item's Coattails" attack to better safeguard e-commerce recommendation systems. At present, there is no previously reported relevant research work conducted specifically for addressing the detection of the "Ride Item's Coattails" attack. In this work, we carried out pioneering work in analyzing and summarizing the characteristics of the false click information produced by attackers on the target products in the "Ride Item's Coattails" attack and designed a set of attack detection techniques suitable for e-commerce recommendation systems. Experimental results on real e-commerce datasets show that our proposed techniques can quickly and effectively detect the large-scale fake click information as well as the associated "Ride Item's Coattails" attack in e-commerce recommendation systems.

While neural end-to-end text-to-speech (TTS) is superior to conventional statistical methods in many ways, the exposure bias problem in the autoregressive models remains an issue to be resolved. The exposure bias problem arises from the mismatch between the training and inference process, that results in unpredictable performance for out-of-domain test data at run-time. To overcome this, we propose a teacher-student training scheme for Tacotron-based TTS by introducing a distillation loss function in addition to the feature loss function. We first train a Tacotron2-based TTS model by always providing natural speech frames to the decoder, that serves as a teacher model. We then train another Tacotron2-based model as a student model, of which the decoder takes the predicted speech frames as input, similar to how the decoder works during run-time inference. With the distillation loss, the student model learns the output probabilities from the teacher model, that is called knowledge distillation. Experiments show that our proposed training scheme consistently improves the voice quality for out-of-domain test data both in Chinese and English systems.

We are witnessing a radical change of attitude towards health information management - the adoption of data analytics to help people monitor and predict health conditions proactively. Most hospitals are aggregating data to provide some sort of analytics as part of their daily practices. Many medical doctors start developing data repositories of their own specialty so that evidence-based clinical decisions can be made, and data of research value can be obtained and exploited efficiently. Health analytics projects need to have software requirements defined for them. These projects must deal with the data itself, the operations performed on the data, and the formatting and distribution of the data for use. The end products of requirements process for a health analytics project will be a set of business and user requirements (both functional, and nonfunctional). It reveals stakeholder needs of analytics results, in terms of how quickly, how often and in what format. This paper shares our observations on the status quo in this area, present the problems and challenges encountered, report the experience and findings in real world projects, and discuss some possible future directions in this area.