Compute-in-Memory (CIM) based on resistive random access memory (RRAM) offers significant advantages in energy efficiency and parallelism, making it a promising solution for accelerating neural networks. However, the computational accuracy, energy efficiency, and flexibility of current CIM chips are still challenged by practical issues such as device and circuit-level non-ideality and the high overhead of peripheral circuits, which remain inadequately addressed in existing designs. To address these challenges, this work proposes REF-CIM, a 40nm robust, energy efficient and flexible RRAM- CIM macro that achieves non-ideality tolerance, high energy efficiency and configurable precision, featuring: 1) a complementary multi-bit input unit (CMIU) with symmetric bit-line access; 2) a proportional current-scaling clamp circuit (PCSC); 3) a distributed tree-based sparse analog-to-digital converter (DTS-ADC); and 4) a configurable multi-mode deployment scheme for supporting diverse neural network precisions. The performance of the proposed macro is evaluated through chip measurements, considering non-ideal effects such as IR-drop, device variation, and analog circuit noise. Simulation results calibrated with measurement data demonstrate a peak energy efficiency of 29.1 TOPS/W@8bIN/8bW/16bOUT, with classification accuracy reaching 92% on the CIFAR-10 dataset under 10% device variation.
H. Ding, Yunfan Yang, Zongwei Wang et al.· IEEE Transactions on Circuit...· 0 citations
Diffusion models have strong generative capabilities. However, their maximum likelihood training objective only focuses on reconstructing the data distribution, making it difficult to align with specific preferences. Reinforcement learning (RL) for preference alignment in diffusion models is promising but limited by reward sparsity. Since a single reward cannot support optimization, existing RL methods usually backpropagate the final reward to all previous steps. However, denoising is stage-wise, with distinct semantics and controllability. Repeating the final reward across all steps creates a temporal objective mismatch, encouraging reward shortcuts that lead to reward hacking. At the same time, due to reward backfilling, each time step receives the same reward, making it impossible to distinguish between actions, thereby weakening the optimization process. To resolve this issue, we propose Stage-Guided Per-Step Optimization (SGPO) for diffusion models, which jointly leverages signal-to-noise ratio and semantic changes to identify generation stages and adaptively assign stage-specific objectives. Early denoising is chaotic and far from the final reward, resulting in weak reward-behavior correlation. This stage should prioritize exiting the chaotic state. In the mid stage, the latent transitions to a stable structure, where the final reward better corresponds to generative behavior. Therefore, this stage optimizes the final reward while exploring diversity to avoid early convergence to a single mode. In the late stage, the latent's core structure is largely fixed, and preference optimization mainly amplifies local details, risking overfitting. Therefore, stable convergence is preferred to avoid quality degradation. Results from 16 comparative experiments validate SGPO. Our method achieves 26.7% average gains in generative quality and 36.7% higher convergence speed.
Renye Yan, Jikang Cheng, You Wu et al.· 0 citations
HEMERA is presented, a heterogeneous memory-centric accelerator for efficient Mamba-2 inference that reformulates the matrix-form SSD computation into an algebraically equivalent streaming-recursive dataflow that avoids quadratic intermediate storage while preserving the original computation.
Hao Ding, Ling Liang, Ruitong Qiao et al.· 0 citations
PAST is proposed, which provides differentiated rewards while adaptively regulating training episode length by jointly perceiving denoising progress and prompt difficulty and establishes a dual adaptive coordination mechanism that balances the extrinsic and intrinsic rewards.
Renye Yan, Jikang Cheng, You Wu et al.· 0 citations