Artificial Intelligence (AI) supported by Deep Artificial Neural Networks (ANNs) is booming and already used in many applications, with impressive results, and we are still its infancy. For many sensing applications it would be advantageous if we could move AI from cloud to Edge. However this requires huge improvements in energy-efficiency. The CONVOLVE project (convolve.eu) aims at enabling smart edge devices through a concerted effort at all layers of the design stack. This ranges from using much more efficient models and mappings, like exploiting Spiking Neural Networks (SNNs), to new processing architectures, like compute-in-memory (CIM), use of approximation, and using new device technology, like memristors. However these latter changes make HW more susceptible to noise and other disturbances. Online continuous learning (i.e. adapting weights) may alleviate these problems. This paper shows several CONVOLVE developments in the crucial areas of CIM architectures, SNN accelerators and online learning.

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Binary Neural Networks (BNNs) have demonstrated significant advantages in reducing computation and memory costs, all while maintaining acceptable accuracy on various image detection tasks. Thus, BNNs have the potential to support practical cognitive tasks on resource-constrained platforms, such as edge computing devices. To realize this, SRAM-based digital Computation-in-Memory (CIM) has gained growing attention as it overcomes the analog CIM architecture bottlenecks such as limited computing accuracy due to process variation, non-linearity, power and area-hungry Analog-to-Digital Converters (ADCs), etc. However, digital CIM architectures are highly dominated by power-hungry adder-trees, which can nullify the benefits of SRAM-based digital CIM. To address this issue, this paper proposes an adder free SRAM-based digital CIM, AFSRAM-CIM, for BNN acceleration. The proposed CIM architecture utilizes a multi-functional 10-T SRAM cell-based crossbar array and a new energy-efficient approach to perform the popcount operation. Simulation results using the MNIST dataset show that the proposed architecture maintains the state-of-the-art inference accuracy of 99.21% with only 11.86 fJ energy per operation. Moreover, AFSRAM-CIM achieves over 3× energy and ≈17× area savings when compared to the conventional digital CIM approaches.@en