Neuromorphic/In-Memory Computing
Our research focuses on memory-based deep learning techniques, including SRAM/ReRAM-based deep learning processing-in-memory (PIM) and Flash memory based robust deep learning inference techniques.
Our research focuses on memory-based deep learning techniques, including SRAM/ReRAM-based deep learning processing-in-memory (PIM) and Flash memory based robust deep learning inference techniques.
![[C65] Continuous Memory Representation for Anomaly Detection](/publication/c65_eccv_2024_memory/featured_huc1405457563e2fe2bcb5178e5675cc30_276005_150x0_resize_q75_h2_lanczos.webp)
![[J30] Reconfigurable Resistive Switching Memory for Telegraph Code Sensing and Recognizing Reservoir Computing Systems](/publication/j30_small_2024/featured_hu392797ac6560e6acaf420b973e8dac8a_104021_150x0_resize_q75_h2_lanczos_3.webp){kind=small}
![[C64] C-AFA: A Conditionally Approximate Full Adder for Efficient DNN Inference in CIM Arrays](/publication/c64_isocc_2024_cim/featured_hu6dcc38f40e51f7b608819649e28410a0_94430_150x0_resize_q75_h2_lanczos_3.webp)
![[C62] Row-Efficient Pruning for In-Memory Convolutional Weight Mapping](/publication/c62_isocc_2024_inmemory/featured_hu1a48ff11825d6800e0d4b8e5718e630b_63812_150x0_resize_q75_h2_lanczos_3.webp)
![[J29] One-Shot Remote Integration of Macromolecular Synaptic Elements on a Chip for Ultrathin Flexible Neural Network System](/publication/j29_adma_2024/featured_hu1beb12e38334fc14a663d962fa3d891c_108785_150x0_resize_q75_h2_lanczos_3.webp)
![[C59] ConvMapSim: Modeling and Simulating Convolutional Weight Mapping for PIM Arrays](/publication/c59_aicas_2024_pim/featured_hu7841e356fe03916c86dab30f71208452_213922_150x0_resize_q75_h2_lanczos_3.webp)
![[C56] TraiNDSim: A Simulation Framework for Comprehensive Performance Evaluation of Neuromorphic Devices for On-Chip Training](/publication/c56_dac_2024/featured_hue7f50756159e4563e731f906898f5e95_159630_150x0_resize_q75_h2_lanczos_3.webp)
![[J28] KERNTROL: Kernel Shape Control Toward Ultimate Memory Utilization for In-Memory Convolutional Weight Mapping](/publication/j28_tcas1_2024/featured_hu805487e99ad06f6fbcaef7b20a0cb065_48999_150x0_resize_q75_h2_lanczos.webp)
![[C55]KARS: Kernel-Grouping Aided Row-Skipping for SDK-based Weight Compression in PIM Arrays](/publication/c55_iscas_2024/featured_hucd18a576234a102f6750bbdb1d150af7_1255458_150x0_resize_q75_h2_lanczos_3.webp)
![[J26] Highly Reliable 3D Channel Memory and Its Application in a Neuromorphic Sensory System for Finger Motion Tracking](/publication/j26_acs_nano_2023/featured_hufa32657bfa793801b399e1667b883191_615630_150x0_resize_q75_h2_lanczos_3.webp)
![[C51] DCR: Decomposition-Aware Column Re-Mapping for Stuck-At-Fault Tolerance in ReRAM Arrays](/publication/c51_iccd_2023/featured_hu955d12e6bf3401e2d14bc5bdf00f703d_492171_150x0_resize_q75_h2_lanczos_3.webp)
![[C47] Kernel Shape Control for Row-Efficient Convolution on Processing-In-Memory Arrays](/publication/c47_iccad_2023/featured_hu1621c99a28d0c8fc51a5ddb8808ca2ed_1230598_150x0_resize_q75_h2_lanczos_3.webp)
![[C45] PAIRS: Pruning-AIded Row-Skipping for Convolutional Weight Mapping in Processing-In-Memory Architectures](/publication/c45_islped_2023/featured_hu5474c00338c99e9bb1e7062de2916f94_1427630_150x0_resize_q75_h2_lanczos_3.webp)
![[C44] Weight-Aware Activation Mapping for Energy-Efficient Convolution on PIM Arrays](/publication/c44_islped_2023/featured_hu055ae0625da78932d12bea10c1b4ed00_233470_150x0_resize_q75_h2_lanczos_3.webp)
![[J24] Room-Temperature-Processable Highly Reliable Resistive Switching Memory with Reconfigurability for Neuromorphic Computing and Ultrasonic Tissue Classification](/publication/j24_afm_2023/featured_hu5a7757ac07928a16d8ffe56aa7f6a65d_841063_150x0_resize_q75_h2_lanczos_3.webp)
![[J18] Multi-Prediction Compression: An Efficient and Scalable Memory Compression Framework for GP-GPU](/publication/j18_cal_2022/featured_hu6aef2193e6bece41bd2a0e680fa624e7_174230_150x0_resize_q75_h2_lanczos.webp)
![[J17] VWC-SDK: Convolutional Weight Mapping Using Shifted and Duplicated Kernel with Variable Windows and Channels](/publication/j17_jetcas_2022/featured_hu1a6fa2935aa5e48aeb8d0d301ce445c0_367892_150x0_resize_q75_h2_lanczos.webp)
![[C32] VW-SDK: Efficient Convolutional Weight Mapping Using Variable Windows for Processing-In-Memory Architectures](/publication/c32_date_2022/Featured_hu6aef2193e6bece41bd2a0e680fa624e7_188891_150x0_resize_q75_h2_lanczos.webp)
![[J15] Adaptive Weight-bit Inversion for State Error Reduction for Robust and Efficient Deep Neural Network Inference Using MLC NAND Flash](/publication/j15_jsa_2022/featured_hu6aef2193e6bece41bd2a0e680fa624e7_64901_150x0_resize_q75_h2_lanczos.webp)
![[C29] A Charge-Domain Computation-in-Memory Macro with Versatile All-Around-Wire-Capacitor for Variable-Precision Computation and Array-Embedded DA/AD Conversions](/publication/c29_esscirc_2021/featured_hu5297bb3ee09acbdf121b1dade851a80c_308541_150x0_resize_q75_h2_lanczos_3.webp){kind=small}
![[J11] A Charge-Domain Scalable-Weight In-Memory Computing Macro with Dual-SRAM Architecture for Precision-Scalable DNN Accelerators](/publication/j11_tcas1_2021/featured_hu6aef2193e6bece41bd2a0e680fa624e7_293978_150x0_resize_q75_h2_lanczos.webp)
![[C26] WISER: Deep Neural Network Weight-bit Inversion for State Error Reduction in MLC NAND Flash](/publication/c26_date_2021/Featured_hu5ad8c31e59524e7f9eeb09f5c1bf4c2c_183210_150x0_resize_q75_h2_lanczos.webp)