Aaron Yen

Aaron Yen, Jooyeon Jeong, Puneet Gupta

As chiplet-based integration advances, designers must select among short-reach die-to-die interconnect technologies with widely varying shoreline and areal bandwidth density, energy per bit, reach, and raw bit error rate (BER). Meeting stringent delivered BER targets in chiplet systems requires error-correcting codes (ECC), but incurs energy, area, and throughput overheads. We develop a flow centered around RTL synthesis power and area estimations to support pathfinding of inter-chiplet links under a stringent 10-27 delivered BER target. We synthesize a parameterized Reed-Solomon code with CRC-64 and Go-Back-N retry logic to estimate the correction overhead for different transceiver bit error rates. Results show that ECC can materially change link comparisons under common figures of merit and that CRC+ARQ can reduce the required RS strength (and decoder overhead) at moderate BERs while still meeting stringent delivered-BER targets. We present a CP-SAT-based link assignment formulation that uses these ECC-corrected metrics under reach, delivered-bandwidth, and shoreline constraints in system-level optimization.

Yutian Ren, Yuqi He, Xuyin Zhang et al.

Machine Learning (ML) has been demonstrated to improve productivity in many manufacturing applications. To host these ML applications, several software and Industrial Internet of Things (IIoT) systems have been proposed for manufacturing applications to deploy ML applications and provide real-time intelligence. Recently, an interactive causality enabled self-labeling method has been proposed to advance adaptive ML applications in cyber-physical systems, especially manufacturing, by automatically adapting and personalizing ML models after deployment to counter data distribution shifts. The unique features of the self-labeling method require a novel software system to support dynamism at various levels. This paper proposes the AdaptIoT system, comprised of an end-to-end data streaming pipeline, ML service integration, and an automated self-labeling service. The self-labeling service consists of causal knowledge bases and automated full-cycle self-labeling workflows to adapt multiple ML models simultaneously. AdaptIoT employs a containerized microservice architecture to deliver a scalable and portable solution for small and medium-sized manufacturers. A field demonstration of a self-labeling adaptive ML application is conducted with a makerspace and shows reliable performance.