The 5G New-Radio (NR) communication standard requires high throughput and low latency, so low-density parity-check (LDPC) codes, which have higher inherent parallelism and lower decoding complexity than turbo codes, were adopted as the main coding method for data channels. In traditional LDPC min-sum decoders, the check node unit was realized using a sorting unit based on the min-tree structure. However, this structure resulted in high hardware complexity and long latency. To address this issue, we propose a new sorting method based on the thermometer code-based number system. Additionally, we introduce a new LDPC decoding architecture that reduces the number of QSN stages from two to one, significantly lowering the shifting logic complexity needed to support different lifting sizes. This is achieved by using relative shift amounts instead of absolute shift amounts specified in the parity check matrix. The proposed decoder implemented using a partially parallel structure in a 65nm CMOS technology satisfies the various operation modes and the throughput requirements of the 5G NR standard, and boasts a higher normalized throughput than state-of-the-art LDPC decoders.

This paper presents a high-speed counter architecture associated with novel LFSR state extension. By employing the proposed state extension, an$\emph {m}$-bit LFSR counter with$(\mathrm{2^{\mathit{m}}}-1)$states is modified to cover$\mathrm{2^{\mathit{m}}}$states without degrading the counting rate. Based on the property that only the low-order bits are frequently switched, the proposed counter consists of two sub-counters to achieve a high counting rate and reduce the hardware complexity needed to convert an LFSR state into a binary state. The low-order sub-counter is implemented with the proposed LFSR counter, and the high-order sub-counter is designed by employing the conventional synchronous binary counter. In addition, the implemented counter takes into account the speed degradation caused by the large fan-out of the high-order sub-counter. The proposed counter designed with standard cells operates at 2.08 GHz in a 65 nm CMOS technology, and its counting rate is almost independent of the counter size.

Recently, convolutional neural networks (CNNs) have actively been applied to computer vision applications such as style transfer that changes the style of a content image into that of a style image. As the style transfer CNNs are based on encoder-decoder network architecture and should deal with high-resolution images that become mainstream these days, the computational complexity and the feature map size are very large, preventing the CNNs from being implemented on an FPGA. This paper proposes a CNN inference accelerator for the style transfer applications, which employs network compression and layer-chaining techniques. The network compression technique is to make a style transfer CNN have low computational complexity and a small amount of parameters, and an efficient data compression method is proposed to reduce the feature map size. In addition, the layer-chaining technique is proposed to reduce the off-chip memory traffic and thus to increase the throughput at the cost of small hardware resources. In the proposed hardware architecture, a neural processing unit is designed by taking into account the proposed data compression and layer-chaining techniques. A prototype accelerator implemented on a FPGA board achieves a throughput comparable to the state-of-the-art accelerators developed for encoder-decoder CNNs.

As the low-density parity-check (LDPC) code has a powerful error-correcting performance and can achieve high throughput, it is being used in many application areas and recently adopted as a channel coding method in the 5G New-Radio communication standard. Unlike other LDPC codes, the 5G LDPC code has various irregular lifting sizes to support diverse message lengths. To meet the demanding requirements of the 5G standard, many solutions have been presented, but all of them are either impractical or fail to satisfy all the requirements. This paper, for the first time, proposes an area-efficient QC-LDPC decoder that satisfies the peak throughput requirements of the 5G standard and supports all the lifting sizes specified in the 5G standard. Instead of relying on full parallelism like in the previous works, this work tries partial parallelism to mitigate the hardware complexity, which leads to high efficiency in hardware complexity. In addition, a novel memory access scheduling method is proposed to solve the data access and alignment problems caused by the partially parallel structure, which is effective in supporting all the lifting sizes. A LDPC decoder realized in 65-nm CMOS technology demonstrates that its decoding throughput is greater than 20Gbps and its area is smaller than the existing decoders.