7 papers
FrequencyFormer: A Co-Designed Sensor-to-Processor Pipeline for Frequency-Domain Vision Transformer Inference
Chengwei Zhou, Ovishake Sen, Xuming Chen +5
Deploying vision transformers (ViTs) on sensor-edge systems is limited not only by on-device compute, but also by the energy and bandwidth required to transmit high-dimensional ima…
In-Memory ADC-Based Nonlinear Activation Quantization for Efficient In-Memory Computing
Shuai Dong, Junyi Yang, Biyan Zhou +3
In deep networks, operations such as ReLU and hardware-driven clamping often cause activations to accumulate near the edges of the distribution, leading to biased clustering and su…
NVM-in-Cache: Repurposing Commodity 6T SRAM Cache into NVM Analog Processing-in-Memory Engine using a Novel Compute-on-Powerline Scheme
Subhradip Chakraborty, Ankur Singh, Xuming Chen +2
The rapid growth of deep neural network (DNN) workloads has significantly increased the demand for large-capacity on-chip SRAM in machine learning (ML) applications, with SRAM arra…
LAWCAT: Efficient Distillation from Quadratic to Linear Attention with Convolution across Tokens for Long Context Modeling
Zeyu Liu, Souvik Kundu, Lianghao Jiang +5
Although transformer architectures have achieved state-of-the-art performance across diverse domains, their quadratic computational complexity with respect to sequence length remai…
A Pathway to Near Tissue Computing through Processing-in-CTIA Pixels for Biomedical Applications
Zihan Yin, Subhradip Chakraborty, Ankur Singh +3
Near-tissue computing requires sensor-level processing of high-resolution images, essential for real-time biomedical diagnostics and surgical guidance. To address this need, we int…
Region Masking to Accelerate Video Processing on Neuromorphic Hardware
Sreetama Sarkar, Sumit Bam Shrestha, Yue Che +3
The rapidly growing demand for on-chip edge intelligence on resource-constrained devices has motivated approaches to reduce energy and latency of deep learning models. Spiking neur…