TranSIC: A Storage in Computing Framework for Designing Efficient Transformer Accelerators in Energy-Constrained Scenarios

Intelligent applications such as wearable health monitors, IoT devices, nano-drones, and embodied intelligence systems are increasingly deployed in energy-constrained environments. Though Transformer models offer advanced processing capabilities, their integration is limited by energy and computational constraints. Extensive research has shown that data movement, particularly for large Transformer weights, is the primary source of energy consumption, accounting for up to 37% of total energy use. Recent techniques like processing in-memory (PIM) and processing near-memory (PNM) are hard to fundamentally reduce the number of data movements, limiting their effectiveness in lowering energy consumption. This paper investigates Storage in Computing (SIC), integrating weight matrices directly into computation circuits, thereby eliminating external memory access during inference and reducing operand data movement significantly. To realize SIC, we propose TranSIC, an innovative approach to create efficient Transformer accelerators with minimized data movement. Key advancements facilitating practical SIC implementation include a reuse strategy to reduce required SIC units and a low-overhead optimization method for this strategy to decrease data routing overhead. Experimental results demonstrate that TranSIC achieves up to 8.65× and 56.24× energy efficiency over state-of-the-art PIM and traditional approaches, respectively.