This paper demonstrates a 17.5-Gb/s complete transceiver with a MaxEye-based autonomous adaptation of equalization in a 40-nm CMOS process. The output stage of the transmitter employs 3-tap finite-impulse response (FIR) equalization. The receiver compensates the attenuation of the cable with a 2-stage continuous time linear equalizer (CTLE) and 5-tap decision-feedback equalizer (DFE). Based on the MaxEye algorithm, the peak gain of CTLE and the tap weights of DFE are adaptively controlled to support a wide range of channel characteristics. A dual-loop clock and data recovery circuit (CDR) using a bang-bang phase detector is implemented. The entire transmitter and receiver respectively consume 90.1 mW and 94.9 mW under a 0.9-V supply while operating at 17.5 Gb/s, respectively.

In this paper, an all-zero block detection scheme is proposed prior to DCT to reduce the encoding complexity for high efficiency video coding (HEVC). Since many coding blocks tend to have all zero coefficients after DCT and quantization, it is worthwhile to detect all-zero-quantized blocks for input residual blocks before DCT so that subsequent transform and quantization can be skipped. Unlike previous coding standards, HEVC adopts large transform sizes such as 16 × 16 and 8 × 8. It becomes more difficult to accurately detect all-zero blocks in HEVC because the large transform blocks contains more variety of content characteristics than smaller ones, thus making it ineffective the existing all-zero block (AZB) detection schemes for large transform blocks in HEVC. In this paper, a novel AZB detection scheme is proposed for the case that Hadamard transform is used as a distortion metric for RDO in HEVC. Statistical upper bounds to be all-zero blocks are derived using the relationship between Walsh Hadamard and DCT transform kernels. Then, a small number of quantized coefficients in a upper left corner of a transform block, which are obtained using the relations between Hadamard transform and DCT, are examined for AZB detection. For 32 × 32 blocks, DC coefficients of 8 × 8 sub-blocks are further examined for AZB detection. The experimental results demonstrate that the proposed scheme detects 87.79% of actual AZBs with 2.87% false alarm rate in average, outperforming the state-of-the-art method. Computational complexity to detect AZB is almost negligible compared to the conventional method.