The expansion of long-context Large Language Models (LLMs) creates significant memory system challenges. While Processing-in-Memory (PIM) is a promising accelerator, we identify that it suffers from critical inefficiencies when scaled to long contexts: severe channel underutilization, performancelimiting I/O bottlenecks, and massive memory waste from static KV cache management. In this work, we propose PIMphony, a PIM orchestrator that systematically resolves these issues with three co-designed techniques. First, Token-Centric PIM Partitioning (TCP) ensures high channel utilization regardless of batch size. Second, Dynamic PIM Command Scheduling (DCS) mitigates the I/O bottleneck by overlapping data movement and computation. Finally, a Dynamic PIM Access (DPA) controller enables dynamic memory management to eliminate static memory waste. Implemented via an MLIR-based compiler and evaluated on a cycle-accurate simulator, PIMphony significantly improves throughput for long-context LLM inference (up to 72B parameters and 1M context length). Our evaluations show performance boosts of up to 11.3× on PIM-only systems and 8.4× on xPU+PIM systems, enabling more efficient deployment of LLMs in real-world long-context applications.

In this work, we present our intelligent SSD, SeeSSD , an energy-efficient computational SSD for a real-time object detection system. SeeSSD embeds an FPGA-based CNN processing engine and the firmware that performs the convolutional operation on the target image. SeeSSD processes the image data at the storage before sending it to the host. This reduces the amount of data transferred to the host and lowers the data movement overhead, thus reducing transfer time and saving power. By using our SeeSSD system and YOLO_Embed, an object detection neural network model, we are able to outperform the fastest YOLO model for an embedded controller, YOLO-Lite, in terms of performance, accuracy, and energy efficiency. YOLO (You Only Look Once) models are a series of one-stage object detection neural models that have become very popular due to their fast speed and high accuracy. The contribution of this work includes designing and implementing our SeeSSD system with a lightweight object detection model, YOLO_Embed, for reducing the data movement overhead, performing real-time inference, and lowering the overall power consumption. We implemented the entire software stack associated with the SeeSSD system; on-device CNN acceleration engine implemented on FPGA, object identification interface for SeeSSD using YOLO_Embed, and embedded software layer in SeeSSD for on-device convolutional processing. We calculated our YOLO_Embed model’s accuracy on object detection dataset benchmarks such as PASCAL VOC 2012, which came out to be 38.1% mAP (mean Accuracy Precision). Our system was able to perform inference in 0.21 seconds while reducing the power consumption by approximately 1.2× and 1.4× for CPU-Only and CPU+GPU systems, respectively. We were also able to reduce the data movement overhead by 24× for a single target image.

•Recap Accelerator-in-Memory (AiM) & AiMX •System Extensions of AiMX Card for Datacenter •AiM & AiMX for On-device AI •Design Choices for Future AiM/AiMX •Conclusion

FPGA is a promising platform in designing a hardware accelerator due to its design flexibility and fast development cycle, despite the device's limited hardware resources. To address this, latest FPGAs have adopted a multi-die architecture providing abundant hardware resources with high yield and cost-benefit. However, the multi-die architecture causes critical timing issues when signal paths cross the die-to-die boundaries, adding another design challenge in using FPGA. We propose OpenMDS, an open-source shell generation framework for high-performance design on multi-die FPGAs. Based on the user's design requirements, it generates an optimized shell for the target FPGA via automated bus pipelining, customized floorplanning, and scalable clocking scheme.