Trusted Executing Environments (TEEs) in hardware designs protect program execution from other untrusted software programs in the processor as well as untrusted off-chip hardware components. Meanwhile, Near-Memory Processing (NMP) has shown performance and energy benefits on memory-intensive workloads. Recently, novel memory encryption schemes have been proposed to allow TEEs to leverage the benefits of NMP without requiring trust in the NMP components. In this paper, we present a system design of confidential computing with NMP that can be directly used in Intel SGX, a TEE platform available in commercial processors today. We develop the full software stack and evaluate the results on commercial processors with the emulated AxDIMM, an FPGA-based NMP platform. In our case study on personalized Deep Learning Recommendation Model (DLRM) inference, the proposed confidential computing in NMP achieves up to 1.51× latency reduction and up to 2.57× throughput improvement.

Transformer-based large language models (LLMs) such as Generative Pre-trained Transformer (GPT) have become popular due to their remarkable performance across diverse applications, including text generation and translation. For LLM training and inference, the GPU has been the predominant accelerator with its pervasive software development ecosystem and powerful computing capability. However, as the size of LLMs keeps increasing for higher performance and/or more complex applications, a single GPU cannot efficiently accelerate LLM training and inference due to its limited memory capacity, which demands frequent transfers of the model parameters needed by the GPU to compute the current layer(s) from the host CPU memory/storage. A GPU appliance may provide enough aggregated memory capacity with multiple GPUs, but it suffers from frequent transfers of intermediate values among GPU devices, each accelerating specific layers of a given LLM. As the frequent transfers of these model parameters and intermediate values are performed over relatively slow device-to-device interconnects such as PCIe or NVLink, they become the key bottleneck for efficient acceleration of LLMs. Focusing on accelerating LLM inference, which is essential for many commercial services, we develop CXL-PNM, a processing near memory (PNM) platform based on the emerging interconnect technology, Compute eXpress Link (CXL). Specifically, we first devise an LPDDR5X-based CXL memory architecture with 512GB of capacity and 1.1TB/s of bandwidth, which boasts 16× larger capacity and 10× higher bandwidth than GDDR6and DDR5-based CXL memory architectures, respectively, under a module form-factor constraint. Second, we design a CXLPNM controller architecture integrated with an LLM inference accelerator, exploiting the unique capabilities of such CXL memory to overcome the disadvantages of competing technologies such as HBM-PIM and AxDIMM. Lastly, we implement a CXLPNM software stack that supports seamless and transparent use of CXL-PNM for Python-based LLM programs. Our evaluation shows that a CXL-PNM appliance with 8 CXL-PNM devices offers 23% lower latency, 31% higher throughput, and 2.8× higher energy efficiency at 30% lower hardware cost than a GPU appliance with 8 GPU devices for an LLM inference service.