Mahesh Mehendale

Adithya Krishna, Sohan Debnath, Madhuvanthi Srivatsav et al.

High-quality, multi-channel neural recording is indispensable for neuroscience research and clinical applications. Large-scale brain recordings often produce vast amounts of data that must be wirelessly transmitted for subsequent offline analysis and decoding, especially in brain-computer interfaces (BCIs) utilizing high-density intracortical recordings with hundreds or thousands of electrodes. However, transmitting raw neural data presents significant challenges due to limited communication bandwidth and resultant excessive heating. To address this challenge, we propose a neural signal compression scheme utilizing Convolutional Autoencoders (CAEs), which achieves a compression ratio of up to 150 for compressing local field potentials (LFPs). The CAE encoder section is implemented on RAMAN, an energy-efficient tinyML accelerator designed for edge computing. RAMAN leverages sparsity in activation and weights through zero skipping, gating, and weight compression techniques. Additionally, we employ hardware-software co-optimization by pruning the CAE encoder model parameters using a hardware-aware balanced stochastic pruning strategy, resolving workload imbalance issues and eliminating indexing overhead to reduce parameter storage requirements by up to 32.4%. Post layout simulation shows that the RAMAN encoder can be implemented in a TSMC 65-nm CMOS process, occupying a core area of 0.0187 mm2 per channel. Operating at a clock frequency of 2 MHz and a supply voltage of 1.2 V, the estimated power consumption is 15.1 uW per channel for the proposed DS-CAE1 model. For functional validation, the RAMAN encoder was also deployed on an Efinix Ti60 FPGA, utilizing 37.3k LUTs and 8.6k flip-flops. The compressed neural data from RAMAN is reconstructed offline with SNDR of 22.6 dB and 27.4 dB, along with R2 scores of 0.81 and 0.94, respectively, evaluated on two monkey neural recordings.

Hassan Dbouk, Hetul Sanghvi, Mahesh Mehendale et al.

Deep neural networks have achieved state-of-the art performance on various computer vision tasks. However, their deployment on resource-constrained devices has been hindered due to their high computational and storage complexity. While various complexity reduction techniques, such as lightweight network architecture design and parameter quantization, have been successful in reducing the cost of implementing these networks, these methods have often been considered orthogonal. In reality, existing quantization techniques fail to replicate their success on lightweight architectures such as MobileNet. To this end, we present a novel fully differentiable non-uniform quantizer that can be seamlessly mapped onto efficient ternary-based dot product engines. We conduct comprehensive experiments on CIFAR-10, ImageNet, and Visual Wake Words datasets. The proposed quantizer (DBQ) successfully tackles the daunting task of aggressively quantizing lightweight networks such as MobileNetV1, MobileNetV2, and ShuffleNetV2. DBQ achieves state-of-the art results with minimal training overhead and provides the best (pareto-optimal) accuracy-complexity trade-off.