Asymmetric Numeral Systems (ANS) play a significant role in learned image codecs (LIC) because of their high coding efficiency. However, it constitutes a substantial portion of inference time, making it the main bottleneck in real-time LIC due to its high computational demands, complex control logic, and serial execution flow. To address these challenges, this paper introduces a hardware-oriented ANS algorithm hANS that reduces complex calculations for state encoding and state-symbol decoding. Furthermore, hANS employs fixed-latency calculation to eliminate control logic, which often causes inconsistent delays. To further enhance throughput, we propose a hardware architecture of ANS for LIC (ANS-LIC), introducing a novel hardware parallelism scheme that incorporates pipeline execution and multi-bin parallelism for encoding, along with multi-stream parallelism for decoding. Additionally, by optimizing the execution order, we achieve a reduction in hardware resource utilization during the decoding process. The proposed ANS-LIC hardware is implemented in RTL and synthesized using TSMC 65nm technology and the Alveo U250 Data Center Accelerator Card. We evaluate ANS-LIC on the Kodak and DIV2K LIC datasets, achieving a 1.17% compression ratio improvement over the SOTA method, Recoil. The implementation results and comparison with other works are presented in Table 1. The synthesis indicates that ANS-LIC requires only 385.5/393.0k gates for encoding and decoding, without SRAM. ANS-LIC achieves throughput improvements of 13.29×/1.47× for encoding and decoding over Recoil. In summary, the proposed ANS-LIC demonstrates substantial advantages.

The automated diagnosis of breast cancer through Whole Slide Images (WSI) is a critical endeavour to combat the threat it poses to women’s health. However, traditional deep learning algorithms strongly rely on Independent and Identically Distributed (I.I.D) and then encounter challenges related to multi-instance bias when analyzing multiple tissue sections from the same patient, limiting their generalization capability. To address this, this study introduces Multi-Instance Bias Suppression (MIBS), a novel approach leveraging adversarial training to mitigate patient-specific overfitting. MIBS employs an instance-level discriminator to guide feature generation, disentangling instance-specific cues from broader diagnostic patterns. Through competitive adversarial training, MIBS enhances feature generalization, effectively addressing overfitting and boosting cross-patient accuracy. Validated on the BreakHis dataset, MIBS effectively tackles multi-instance bias-induced overfitting. By bridging the gap between cutting-edge deep learning techniques and the challenges posed by large-scale medical image data, MIBS advances the accuracy and applicability of breast cancer diagnosis. Our approach addresses the multi-instance bias challenge and integrates seamlessly with big data, propelling medical image analysis to new heights of efficiency and precision.