We present a reconfigurable and scalable current-based analog compute-in-memory (CIM) macro based on a split dual-port 8T (DP-8T) SRAM bitcell. This bitcell can perform MAC operations between signed 9-bit inputs and signed 9-bit weights and logical operations. Throughput is enhanced by sensing MAC and logical operations from both true and complementary bitcell sides via dual bitline sensing, without requiring external circuitry to decode the MAC output from the complementary side. To improve the signal margin and reduce the number of ADCs, we split the 8-bit magnitudes of inputs and weights into 2-bit groups and perform efficient weight encoding with a 2C–1C network combined with analog shift-and-add using 4C and 1C at each bitline. The proposed$64\times 128$macro performs 4096 signed MAC operations in 4 cycles with a latency of 16.2 ns. The design is implemented in TSMC 65-nm technology at 1.2 V. The architecture performs linear MAC operations across different inputs, weights, and process corners. It delivers a throughput of 252.83 GOPS, an MAC energy efficiency of 10.11 TOPS/W, and a signal margin of 26 mV for signed MAC operations. In the logical compute mode, the architecture performsnor,and,nand, andorBoolean operations in a single cycle, as well asexnorandexorusing additional logical gates, achieving a throughput of 3276.8 GOPS and a latency of 1.25 ns at 0.8 V. The work achieves inference accuracies of 99.1%, 91.65%, and 71.8% on MNIST, CIFAR-10, and CIFAR-100, respectively.

In this paper, we address detection of activities in long-term untrimmed videos. Detecting temporal delineation of activities is important to analyze large-scale videos. However, there are still challenges yet to be overcome in order to have an accurate temporal segmentation of activities. Detection of daily-living activities is even more challenging due to their high intra-class and low inter-class variations, complex temporal relationships of sub-activities performed in realistic settings. To tackle these problems, we propose an online activity detection framework based on the discovery of sub-activities. We consider a long-term activity as a sequence of short-term sub-activities. Then we utilize a weakly supervised classifier trained on discovered sub-activities which allows us to predict an ongoing activity before being completely observed. To achieve a more precise segmentation a greedy post-processing technique based on Markov models is employed. We evaluate our framework on DAHLIA and GAADRD daily living activity datasets where we achieve state-of-the-art results on detection of activities.

Many approaches were proposed to solve the problem of activity recognition in short clipped videos, which achieved impressive results with hand-crafted and deep features. However, it is not practical to have clipped videos in real life, where cameras provide continuous video streams in applications such as robotics, video surveillance, and smart-homes. Here comes the importance of activity detection to help recognizing and localizing each activity happening in long videos. Activity detection can be defined as the ability to localize starting and ending of each human activity happening in the video, in addition to recognizing each activity label. A more challenging category of human activities is the daily-living activities, such as eating, reading, cooking, etc, which have low inter-class variation and environment where actions are performed are similar. In this work we focus on solving the problem of detection of daily-living activities in untrimmed video streams. We introduce new online activity detection pipeline that utilizes single sliding window approach in a novel way; the classifier is trained with sub-parts of training activities, and an online frame-level early detection is done for sub-parts of long activities during detection. Finally, a greedy Markov model based post processing algorithm is applied to remove false detection and achieve better results. We test our approaches on two daily-living datasets, DAHLIA and GAADRD, outperforming state of the art results by more than 10%.