publications
publications by categories in reversed chronological order. generated by jekyll-scholar.
2026
- IISWCAmir Taherin, Sana Taghipour Anvari, Charles Amante, and 9 more authorsIn IEEE International Symposium on Workload Characterization (IISWC). To appear , 2026
Edge LLM deployment is shaped by more than model size and precision: inference backend, hardware platform, memory traffic, and power management all affect latency and efficiency. We present Hydra, a common-schema, phase-aware workload characterization framework for LLM inference on edge SoCs. Hydra instruments HuggingFace Transformers and llama.cpp with a shared per-prompt timing schema and fuses those records with hardware telemetry, enabling a multi-dimensional characterization of performance, system-resource utilization, and efficiency across prefill and decode phases. Using Hydra, we evaluate three consecutive edge System-on-Chip (SoC) generations (AGX Xavier, AGX Orin, and AGX Thor), 13 instruction-tuned LLMs from seven families, five execution formats, and consider input/output-length sensitivity. The resulting artifact contains roughly 107K per-prompt records and will be released with Hydra. Our analysis shows that aggregate latency alone hides key deployment effects: backend structure changes where latency is introduced, quantization reduces memory traffic and energy but does not predict power monotonically, and SoC generation changes how utilization and efficiency should be interpreted. By connecting phase-level timing with system-resource utilization and efficiency metrics, Hydra enables reproducible, phase-aware characterization of edge LLM inference.
@inproceedings{taherin2026hydra, title = {Hydra: Phase-Aware Workload Characterization of LLM Inference across Edge SoC Generations, Backends, and Quantization Levels}, author = {Taherin, Amir and Taghipour Anvari, Sana and Amante, Charles and Chen, Yixiao and Noroian, Ruben and Feric, Zlatan and Bohm Agostini, Nicolas and Zhao, Pu and Cano, José and Ren, Bin and Wang, Yanzhi and Kaeli, David}, booktitle = {IEEE International Symposium on Workload Characterization (IISWC)}, year = {2026}, } - IISWCZlatan Feric, Amir Taherin, Bin Ren, and 3 more authorsIn IEEE International Symposium on Workload Characterization (IISWC). To appear , 2026
We present RAGMark, a modular benchmarking framework for advanced Retrieval-Augmented Generation (RAG) systems targeting small-scale multi-GPU environments. RAGMark evaluates diverse RAG components, including retrievers, vector databases, prompt-processing methods, and generator models, while collecting detailed per-stage metrics such as latency, GPU utilization, memory consumption, power usage, time to first token (TTFT), throughput, and answer quality. The framework is highly extensible, separating RAG stages, timing, and resource monitoring into modular components, and is designed to efficiently sweep large configuration spaces while minimizing repeated model and database initialization overhead. Using RAGMark, we characterize five RAG workloads on open-domain QA datasets across varying retrieval depths, model scales, reranking, compression methods, and vector database configurations. We show that while autoregressive generation dominates latency in naive pipelines, context-reduction techniques shift bottlenecks across compute, memory bandwidth, and preprocessing stages. Reranking and compression produce compounding benefits: reranking reduces compression workload itself, while both jointly reduce prefill and KV-cache traversal costs, lowering energy consumption by up to 66%. We further observe strong cross-stage interactions, where small upstream context reductions cascade through downstream latency, memory traffic, and energy consumption.
@inproceedings{feric2026ragmark, title = {RAGMark: A Comprehensive Framework for Benchmarking Retrieval-Augmented Generation Systems}, author = {Feric, Zlatan and Taherin, Amir and Ren, Bin and Wang, Yanzhi and Dy, Jennifer and Kaeli, David}, booktitle = {IEEE International Symposium on Workload Characterization (IISWC)}, year = {2026}, } - SECAmir Taherin, José Cano, Bin Ren, and 2 more authorsIn ACM/IEEE Symposium on Edge Computing (SEC). To appear , 2026
Video object detection on edge devices requires running computationally expensive detectors over long frame streams, leading to high energy consumption and sustained GPU utilization. Although consecutive video frames contain substantial temporal redundancy, naive frame-skipping strategies are content-blind: they skip during critical moments such as object entry, occlusion recovery, and abrupt motion, causing substantial detection-quality loss. We present ALBIREO, a detector-agnostic, codec-free, adaptive inference framework that wraps off-the-shelf object detectors and decides when detector invocation can be safely skipped based on scene content and per-object temporal state. ALBIREO requires no detector modification or detector retraining and is designed as a drop-in efficiency layer for improving the accuracy-energy-latency tradeoff of edge video-detection pipelines. ALBIREO maintains a 10-dimensional Kalman filter (KF) for each active object state and invokes the detector only when prediction uncertainty exceeds a threshold. On skipped frames, bounding boxes are predicted from the KF state at near-zero GPU cost. A KF-based rescue mechanism preserves confirmed object states through brief detector misses, and a lightweight empty-scene screen further avoids full detector calls on objectless frames. We evaluate ALBIREO on the BDD100K MOT validation split using three architecturally distinct detectors (YOLO11x, YOLO26x, and RF-DETR-Large) on two NVIDIA Jetson platforms (AGX Thor and AGX Orin). Across all detector-platform configurations, ALBIREO keeps AP@50 within ±1.2 pp of per-frame inference while reducing total energy by 12.1-17.6%. On the primary YOLO26x configuration, ALBIREO improves AP@50 by +0.8 pp while reducing energy by 17.6% on Thor and 14.4% on Orin, and reduces per-frame energy-delay product by 24.9% on Thor and 26.1% on Orin.
@inproceedings{taherin2026albireo, title = {ALBIREO: Adaptive, Energy-Efficient Inference Framework for Video Object Detection on the Edge}, author = {Taherin, Amir and Cano, José and Ren, Bin and Wang, Yanzhi and Kaeli, David}, booktitle = {ACM/IEEE Symposium on Edge Computing (SEC)}, year = {2026}, } - AI SummitZlatan Feric*, Amir Taherin*, Yanzhi Wang, and 1 more authorIn Proceedings of the ACM AI Leadership Summit. To appear , 2026
Retrieval-augmented generation (RAG) improves language-model responses by grounding generation in external passages, which comes with overhead: retrieved context lengthens the prompt, increasing prefill work, KV-cache footprint, memory traffic, latency, and energy. Context compression offers a natural remedy by pruning retrieved text before generation. However, state-of-the-art context-compression methods are typically used with a fixed compression budget, or with the rate selected offline and then applied at inference time. This static view ignores both workload variation and the live state of the edge device. On an edge SoC, compression is not free: the compressor itself runs on the same SoC and consumes latency and energy that can offset any generation savings. This paper proposes a vision for telemetry-informed adaptive compression in edge RAG, grounded in experimental evidence. We characterize the compression tradeoff on the NVIDIA Jetson AGX Thor using Llama and Qwen generators, Natural Questions and HotpotQA datasets, and LLMLingua-2 compression. Our measurements show that generation dominates the RAG budget for larger models, reaching roughly 90% of per-query latency and 91% of GPU energy for 7B-8B generators. Exploring the impact of the compression rate reveals an adaptive operating region: mild compression can miss energy opportunities, and overly aggressive compression can hurt inference quality. Intermediate compression can reduce GPU energy by up to 53.2%, and SoC energy by up to 48.2%, with negligible quality loss. We argue for runtime policies that dynamically manage compression, guided by workload features and edge telemetry.
@inproceedings{feric2026retrieved, title = {From Retrieved Context to Runtime Control: Adaptive Compression for Edge-based RAG}, author = {Feric, Zlatan and Taherin, Amir and Wang, Yanzhi and Kaeli, David}, booktitle = {Proceedings of the ACM AI Leadership Summit}, year = {2026}, } - GLSVLSIAmir Taherin, Juyi Lin, Arash Akbari, and 5 more authorsIn Proceedings of the Great Lakes Symposium on VLSI (GLSVLSI), 2026
Vision-Language-Action (VLA) models have emerged as powerful generalist policies for robotic control, yet their performance scaling across model architectures and hardware platforms, as well as their associated power budgets, remain poorly understood. This work presents an evaluation of five representative VLA models—spanning state-of-the-art baselines and two newly proposed architectures—targeting edge and datacenter GPU platforms. Using the LIBERO benchmark, we measure accuracy alongside system-level metrics, including latency, throughput, and peak memory usage, under varying edge power constraints and high-performance datacenter GPU configurations. Our results identify distinct scaling trends: (1) architectural choices, such as action tokenization and model backbone size, strongly influence throughput and memory footprint; (2) power-constrained edge devices exhibit non-linear performance degradation, with some configurations matching or exceeding older datacenter GPUs; and (3) high-throughput variants can be achieved without significant accuracy loss. These findings provide actionable insights when selecting and optimizing VLAs across a range of deployment constraints. Our work challenges current assumptions about the superiority of datacenter hardware for robotic inference.
@inproceedings{taherin2026crossplatform, title = {Cross-Platform Scaling of Vision-Language-Action Models from Edge to Cloud GPUs}, author = {Taherin, Amir and Lin, Juyi and Akbari, Arash and Akbari, Arman and Zhao, Pu and Chen, Weiwei and Kaeli, David and Wang, Yanzhi}, booktitle = {Proceedings of the Great Lakes Symposium on VLSI (GLSVLSI)}, year = {2026}, doi = {10.1145/3787109.3816400}, } - arXivTimothy Rupprecht*, Pu Zhao*, Amir Taherin*, and 20 more authors2026
This report of world models distinguishes prior works by the cognitive functions they innovate. Many works claim an almost human-like cognitive capability in their world models. To evaluate these claims requires a proper grounding in first principles from human and machine cognition theory. In moving towards human-like world models we present a conceptual unified framework for world models that fully incorporates all the cognitive functions (i.e., memory, perception, language, reasoning, imagining, motivation, and metacognition) and identify gaps in existing research as a guide for future states of the art. In particular, we find that motivation (especially intrinsic motivation) and metacognition remain drastically under-researched, and we propose concrete directions to address these gaps informed by active inference and global workspace theory. We also introduce epistemic world models, a new category encompassing agent frameworks for scientific discovery that operate over structured knowledge. Our taxonomy, applied to video, embodied, and epistemic world models, suggests research directions where prior taxonomies have not.
@misc{rupprecht2026human, title = {Human Cognition in Machines: A Unified Perspective of World Models}, author = {Rupprecht, Timothy and Zhao, Pu and Taherin, Amir and Akbari, Arash and Akbari, Arman and He, Yumei and Imtiaz, Tooba and Duffy, Sean and Lin, Juyi and Chen, Yixiao and Chowdhury, Rahul and Nan, Enfu and Shen, Yixin and Cao, Yifan and Zeng, Haochen and Chen, Weiwei and Yuan, Geng and Dy, Jennifer and Ostadabbas, Sarah and Zhang, Xuan and Kaeli, David and Yeh, Edmund and Wang, Yanzhi}, year = {2026}, }
2025
- arXivJuyi Lin, Amir Taherin, Arash Akbari, and 11 more authors2025
Recent large-scale Vision Language Action (VLA) models have shown superior performance in robotic manipulation tasks guided by natural language. However, current VLA models suffer from two drawbacks: (i) generation of massive tokens leading to high inference latency and increased training cost, and (ii) insufficient utilization of generated actions resulting in potential performance loss. To address these issues, we develop a training framework to finetune VLA models for generating significantly fewer action tokens with high parallelism, effectively reducing inference latency and training cost. Furthermore, we introduce an inference optimization technique with a novel voting-based ensemble strategy to combine current and previous action predictions, improving the utilization of generated actions and overall performance. Our results demonstrate that we achieve superior performance compared with state-of-the-art VLA models, achieving significantly higher success rates and 39x faster inference than OpenVLA with 46 Hz throughput on edge platforms, demonstrating practical deployability.
@misc{lin2025vote, title = {VOTE: Vision-Language-Action Optimization with Trajectory Ensemble Voting}, author = {Lin, Juyi and Taherin, Amir and Akbari, Arash and Akbari, Arman and Lu, Lei and Chen, Guangyu and Padir, Taskin and Yang, Xiaomeng and Chen, Weiwei and Li, Yiqian and Lin, Xue and Kaeli, David and Zhao, Pu and Wang, Yanzhi}, year = {2025}, }
2021
- DSNAmir Taherin, Tirthak Patel, Giorgis Georgakoudis, and 2 more authorsIn 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 2021
@inproceedings{taherin2021examining, title = {Examining Failures and Repairs on Supercomputers with Multi-GPU Compute Nodes}, author = {Taherin, Amir and Patel, Tirthak and Georgakoudis, Giorgis and Laguna, Ignacio and Tiwari, Devesh}, booktitle = {51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)}, year = {2021}, doi = {10.1109/DSN48987.2021.00043}, }
2020
- FPGAQiuyue Sun, Amir Taherin, Yawo Siatitse, and 1 more authorIn Proceedings of the 2020 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA), 2020
@inproceedings{sun2020energy, title = {Energy-Efficient 360-Degree Video Rendering on FPGA via Algorithm-Architecture Co-Design}, author = {Sun, Qiuyue and Taherin, Amir and Siatitse, Yawo and Zhu, Yuhao}, booktitle = {Proceedings of the 2020 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA)}, year = {2020}, doi = {10.1145/3373087.3375317}, }
2018
- TSUSCAmir Taherin, Mohammad Salehi, and Alireza EjlaliIEEE Transactions on Sustainable Computing, 2018
@article{taherin2018reliability, title = {Reliability-Aware Energy Management in Mixed-Criticality Systems}, author = {Taherin, Amir and Salehi, Mohammad and Ejlali, Alireza}, journal = {IEEE Transactions on Sustainable Computing}, year = {2018}, doi = {10.1109/TSUSC.2018.2801123} }
2015
- RTESTAmir Taherin, Mohammad Salehi, and Alireza EjlaliIn CSI Symposium on Real-Time and Embedded Systems and Technologies (RTEST), 2015
@inproceedings{taherin2015stretch, title = {Stretch: Exploiting Service Level Degradation for Energy Management in Mixed-Criticality Systems}, author = {Taherin, Amir and Salehi, Mohammad and Ejlali, Alireza}, booktitle = {CSI Symposium on Real-Time and Embedded Systems and Technologies (RTEST)}, year = {2015}, doi = {10.1109/RTEST.2015.7369846} }