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cs.AR2024
Topkima-Former: Low-energy, Low-Latency Inference for Transformers using top-k In-memory ADC
Shuai Dong, Junyi Yang, Xiaoqi Peng +5
Transformer model has gained prominence as a popular deep neural network architecture for neural language processing (NLP) and computer vision (CV) applications. However, the exten…
eess.SP2024
A Low-Power Spike Detector Using In-Memory Computing for Event-based Neural Frontend
Ye Ke, Arindam Basu
With the sensor scaling of next-generation Brain-Machine Interface (BMI) systems, the massive A/D conversion and analog multiplexing at the neural frontend poses a challenge in ter…
eess.SP2024
Hybrid Event-Frame Neural Spike Detector for Neuromorphic Implantable BMI
Vivek Mohan, Wee Peng Tay, Arindam Basu
This work introduces two novel neural spike detection schemes intended for use in next-generation neuromorphic brain-machine interfaces (iBMIs). The first, an Event-based Spike Det…