Shifted-and-Duplicated-Kernel (SDK) mapping has emerged as an effective strategy to accelerate convolutional layers on compute-in-memory (CIM) hardware. However, existing SDK variants (e.g., VWC-SDK) merely optimize mapping for a single CIM macro, leaving inter-macro parallelism unexplored. Moreover, their mapping methodologies are still suboptimal. To address these limitations, we present TetrisG-SDK, a novel framework that employs adaptive windows to boost mapping performance. The proposed windows accommodate more input channels, increase array utilization at marginal space, and adapt to different channel depths. More importantly, TetrisG-SDK reduces compute latency by searching for optimal window configurations across multiple CIM macros with a fixed hardware budget. Besides, it incorporates grouped convolution to further decrease computing cycles while maintaining near-lossless model accuracy. In addition, TetrisG-SDK integrates a validated CIM hardware simulator to provide accurate system-/application-level estimations of latency, area and energy. Compared to the single-macro VWC-SDK, the proposed framework achieves a speed-up by 1.2x, 1.3x, and 1.3x for CNN8, GoogLeNet Inception, and DenseNet40 models, respectively. When deployed on the simulator, it reduces system-level latency and energy by 2.4x and 1.7x for CNN8, 1.3x and 1.2x for Inception, and 1.3x and 1.6x for DenseNet40, respectively. When leveraging macro-level parallelism, TetrisG-SDK reduces the Energy-Delay-Area-Product (EDAP) by 70% for CNN8, 68% for Inception, and 36% for DenseNet40 compared to its non-grouped counterpart. These results manifest that TetrisG-SDK is a promising solution to efficiently mapping convolutional layers on CIM hardware.

翻译：移位-复制核（SDK）映射已成为加速存内计算（CIM）硬件上卷积层的有效策略。然而，现有SDK变体（例如VWC-SDK）仅针对单一CIM宏单元优化映射，忽略了宏间并行性。此外，其映射方法仍非最优。为解决这些局限，我们提出TetrisG-SDK——一种采用自适应窗口提升映射性能的新型框架。所提窗口可容纳更多输入通道，在极小空间内增加阵列利用率，并适应不同通道深度。更重要的是，TetrisG-SDK通过搜索固定硬件预算下多个CIM宏单元的最优窗口配置，有效降低了计算延迟。此外，它引入分组卷积，在保持近乎无损模型精度的同时进一步减少计算周期。同时，TetrisG-SDK集成了经过验证的CIM硬件模拟器，可提供精确的系统/应用级延迟、面积和能耗评估。相较于单宏单元VWC-SDK，所提框架在CNN8、GoogLeNet Inception和DenseNet40模型上分别实现1.2倍、1.3倍和1.3倍的加速。部署于模拟器时，CNN8的系统级延迟和能耗分别降低2.4倍和1.7倍，Inception降低1.3倍和1.2倍，DenseNet40降低1.3倍和1.6倍。利用宏级并行性时，与非分组版本相比，TetrisG-SDK将CNN8、Inception和DenseNet40的能耗-延迟-面积乘积（EDAP）分别降低了70%、68%和36%。这些结果表明，TetrisG-SDK是高效映射CIM硬件上卷积层的一种有前景的方案。