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تسجيل مستخدم جديدTemporal State Machines: Using temporal memory to stitch time-based graph computations
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Race logic, an arrival-time-coded logic family, has demonstrated energy and performance improvements for applications ranging from dynamic programming to machine learning. However, the ad hoc mappings of algorithms into hardware result in custom architectures making them difficult to generalize. We systematize the development of race logic by associating it with the mathematical field called tropical algebra. This association between the mathematical primitives of tropical algebra and generalized race logic computations guides the design of temporally coded tropical circuits. It also serves as a framework for expressing high level timing-based algorithms. This abstraction, when combined with temporal memory, allows for the systematic generalization of race logic by making it possible to partition feed-forward computations into stages and organizing them into a state machine. We leverage analog memristor-based temporal memories to design a such a state machine that operates purely on time-coded wavefronts. We implement a version of Dijkstras algorithm to evaluate this temporal state machine. This demonstration shows the promise of expanding the expressibility of temporal computing to enable it to deliver significant energy and throughput advantages.
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We extend the reach of temporal computing schemes by developing a memory for multi-channel temporal patterns or wavefronts. This temporal memory re-purposes conventional one-transistor-one-resistor (1T1R) memristor crossbars for use in an arrival-tim
e coded, single-event-per-wire temporal computing environment. The memristor resistances and the associated circuit capacitances provide the necessary time constants, enabling the memory array to store and retrieve wavefronts. The retrieval operation of such a memory is naturally in the temporal domain and the resulting wavefronts can be used to trigger time-domain computations. While recording the wavefronts can be done using standard digital techniques, that approach has substantial translation costs between temporal and digital domains. To avoid these costs, we propose a spike timing dependent plasticity (STDP) inspired wavefront recording scheme to capture incoming wavefronts. We simulate these designs with experimentally validated memristor models and analyze the effects of memristor non-idealities on the operation of such a memory.
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We propose a dedicated winner-take-all circuit to efficiently implement the intra-column competition between cells in Hierarchical Temporal Memory which is a crucial part of the algorithm. All inputs and outputs are charge-based for compatibility wit
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Extracting spatial-temporal knowledge from data is useful in many applications. It is important that the obtained knowledge is human-interpretable and amenable to formal analysis. In this paper, we propose a method that trains neural networks to lear
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