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  "title": "Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models",
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    "Optimization Strategies for Enhancing Resource Efficiency in Transformers and Large Language Models"
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  "authors": [
    "Tom Wallace",
    "Beatrice M. Ombuki-Berman",
    "Naser Ezzati-Jivan"
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    "venue": "ACM/SPEC International Conference on Performance Engineering (ICPE)",
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    "issue": null,
    "pages": "105-112",
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  "description": "The paper compares compression and optimization strategies for reducing the resource cost of Transformer and large-language-model workloads while retaining useful accuracy.",
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    "core_contribution": "The paper compares compression and optimization strategies for reducing the resource cost of Transformer and large-language-model workloads while retaining useful accuracy.",
    "problem": "Transformer and LLM size increases raise computation, energy, and memory costs. The paper compares compression methods under a common objective so that energy savings and runtime reductions can be weighed against perplexity and task-performance loss.",
    "method": "The study tests 8/4-bit quantization, knowledge distillation, attention-head pruning at 90% and 80% thresholds, SparseGPT-style magnitude/structured pruning, and hybrid models including MiniLLM, NVIDIA Minitron, and ShearedLlama. It introduces opt = P_c^1.5 (alpha T_c + beta E_c), with adjustable time and energy weights, and reports standalone and hybrid evaluations on GPT-2/OPT/LLaMA-family models.",
    "findings": "On GPT-2 125M, 4-bit quantization increases perplexity 3.79%, runtime 4.86%, and reduces energy 50.21%; 8-bit reduces energy 56.28% but increases runtime 203.78%. On GPT-2 Large, 8-bit reduces energy 69.17% with a -0.04% perplexity change but increases runtime 83.59%. The paper reports that 4-bit quantization plus distillation and Minitron-style hybrids provide the most favorable energy/performance trade-offs, while attention-head pruning often loses too much perplexity.",
    "limitations": "The study uses one local machine and does not report OS, kernel, CUDA, Python, library, or model-checkpoint versions. GPU-compatible and CPU-only methods use different hardware paths; pruning is repeated only five times versus 30 for other model/dataset pairs. The evaluation does not establish production-scale training cost or broad generalization beyond the listed models, datasets, and benchmarks.",
    "future_work": "Refine the objective with training energy and more detailed cost terms; evaluate more architectures and benchmarks; investigate retraining to recover perplexity after compression; and improve quantization implementations whose runtime cost can dominate the energy benefit."
  },
  "tags": [
    "llm-efficiency",
    "energy-efficiency",
    "model-compression",
    "performance-engineering"
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    "transformers",
    "quantization",
    "knowledge distillation",
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    "4-bit quantization",
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    "sustainable AI"
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