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Found 5 papers, 3 papers with code
KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization
LLMs are seeing growing use for applications such as document analysis and summarization which require large context windows, and with these large context windows KV cache activations surface as the dominant contributor to memory consumption during inference.
MoCA: Memory-Centric, Adaptive Execution for Multi-Tenant Deep Neural Networks
Driven by the wide adoption of deep neural networks (DNNs) across different application domains, multi-tenancy execution, where multiple DNNs are deployed simultaneously on the same hardware, has been proposed to satisfy the latency requirements of different applications while improving the overall system utilization.
Full Stack Optimization of Transformer Inference: a Survey
In this work, we survey different approaches for efficient Transformer inference, including: (i) analysis and profiling of the bottlenecks in existing Transformer architectures and their similarities and differences with previous convolutional models; (ii) implications of Transformer architecture on hardware, including the impact of non-linear operations such as Layer Normalization, Softmax, and GELU, as well as linear operations, on hardware design; (iii) approaches for optimizing a fixed Transformer architecture; (iv) challenges in finding the right mapping and scheduling of operations for Transformer models; and (v) approaches for optimizing Transformer models by adapting the architecture using neural architecture search.
CoSA: Scheduling by Constrained Optimization for Spatial Accelerators
Recent advances in Deep Neural Networks (DNNs) have led to active development of specialized DNN accelerators, many of which feature a large number of processing elements laid out spatially, together with a multi-level memory hierarchy and flexible interconnect.
Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack Integration
DNN accelerators are often developed and evaluated in isolation without considering the cross-stack, system-level effects in real-world environments.
