Daily | Yixun Hong

ADEPT: Architecture-Driven Energy-Efficient CNN Fine-Tuning on PIM Accelerators

Pratyush Dhingra, Vibhanshu Sharma, Janardhan Rao Doppa, Partha Pratim Pande 2026-07-22

Hardware Codesign Compiler Runtime Architecture × Accelerator

ADEPT addresses the problem of high computational and memory overhead in CNN fine-tuning on PIM accelerators, caused by frequent off-chip memory access for intermediate activations. The method introduces a hardware-aware framework that adaptively trains models using a novel metric balancing gradient-based sensitivity with architecture-specific Energy-Delay Product (EDP). Experimental evidence shows ADEPT reduces total trainable parameters and off-chip data access during fine-tuning with minimal loss in predictive accuracy compared to full-parameter fine-tuning. This matters because it enables energy-efficient fine-tuning on PIM accelerators, making edge deployment of CNNs more practical without sacrificing model accuracy.

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A Reinforcement-Learning-Augmented Liquid-Fueled Reactor Network Model for Predicting Lean Blowout in Gas Turbine Combustors

Philip John, Eloghosa Ikponmwoba, Pinaki Pal, Opeoluwa Owoyele 2026-07-22

Design Space Exploration Agentic Architecture × Network

The problem is that existing methods for determining cluster boundaries in reactor network models rely on manual heuristics or distance-based metrics, which are not optimized for target metrics like lean blowout (LBO) prediction accuracy. The method introduces a reinforcement learning (RL) framework that uses a multi-stage clustering-classification strategy, where an actor-critic RL agent merges micro-clusters into optimal reactor zones to explicitly improve LBO prediction. Experimental evidence using a Jet-A mechanism (119 species, 841 reactions) shows the RL framework improves predictive fidelity over k-means clustering and captures correct LBO trends while achieving substantial speedups relative to high-fidelity simulations. This matters because the RL-driven approach provides a computationally efficient reduced-order modeling technique that can complement high-fidelity simulations for rapid design-space exploration in gas turbine combustors.

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High-Level Synthesis of Efficient Pipelines with Visibility Control

Jungin Rhee, Minseong Jang, Jaewoo Kim, Jeehoon Kang 2026-07-22

Design Space Exploration Agentic Architecture ×

The problem is that existing high-level synthesis (HLS) tools for pipelining either lack fine-grained control over pipeline structure and hazard resolution or sacrifice sequential semantics to provide it. The method introduces an HLS tool based on visibility control, a programming abstraction that unifies hazard resolution strategies such as stalling, bypassing, speculation, deferred commit, and register renaming within a sequential programming model. Experimental evidence on in-order RISC-V cores, histograms, and an AES accelerator shows that compiled pipelines outperform HLS tools with sequential semantics and achieve power, performance, and area (PPA) comparable to hand-written RTL. This matters because it enables rapid design-space exploration for efficient pipelines without requiring RTL or concurrent programming models.

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Cross-Domain Acceleration of Open Modification Search: From Commodity Platforms to Emerging Memory and Storage Devices

Sumukh Pinge, Chang Eun Song, Po-Kai Hsu, Zheyu Li 2026-07-22

Search Data Workload Accelerator Architecture × GPU

The problem is that open modification search (OMS) in mass spectrometry is data-intensive and limited by reference data movement, not computation. The method introduces a binary hyperdimensional computing (HDC)-based OMS formulation that reduces similarity evaluation to lightweight bitwise primitives and tolerates device-level non-idealities. Experimental evidence shows that memory- and storage-centric architectures deliver up to >100x speedup and >40,000x improvement in energy efficiency over commodity platforms. This matters because it identifies memory- and storage-centric architectures as a key breakthrough for large-scale, high-speed search acceleration.

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