SRAM cell stability will be a primary concern for future technologies due to variability and decreasing power supply voltages. 6T-SRAM can be optimized for stability by choosing the cell layout, device threshold voltages, and the ß ratio. 8T-SRAM, however, provides a much greater enhancement in stability by eliminating cell disturbs during a read access, thus facilitating continued technology scaling. We demonstrate the smallest 6T (0.124µm 2 half-cell) and full 8T (0.1998µm 2 ) cells to date.
Abstract-Efforts to achieve the long-standing dream of realizing scalable learning algorithms for networks of spiking neurons in silicon have been hampered by (a) the limited scalability of analog neuron circuits; (b) the enormous area overhead of learning circuits, which grows with the number of synapses; and (c) the need to implement all inter-neuron communication via off-chip address-events. In this work, a new architecture is proposed to overcome these challenges by combining innovations in computation, memory, and communication, respectively, to leverage (a) robust digital neuron circuits; (b) novel transposable SRAM arrays that share learning circuits, which grow only with the number of neurons; and (c) crossbar fan-out for efficient on-chip inter-neuron communication. Through tight integration of memory (synapses) and computation (neurons), a highly configurable chip comprising 256 neurons and 64K binary synapses with on-chip learning based on spike-timing dependent plasticity is demonstrated in 45nm SOI-CMOS. Near-threshold, event-driven operation at 0.53V is demonstrated to maximize power efficiency for real-time pattern classification, recognition, and associative memory tasks. Future scalable systems built from the foundation provided by this work will open up possibilities for ubiquitous ultra-dense, ultra-low power brain-like cognitive computers.
| After decades of continuous scaling, further advancement of silicon microelectronics across the entire spectrum of computing applications is today limited by power dissipation. While the trade-off between power and performance is well-recognized, most recent studies focus on the extreme ends of this balance. By concentrating instead on an intermediate range, an $ 8Â improvement in power efficiency can be attained without system performance loss in parallelizable applicationsVthose in which such efficiency is most critical. It is argued that power-efficient hardware is fundamentally limited by voltage scaling, which can be achieved only by blurring the boundaries between devices, circuits, and systems and cannot be realized by addressing any one area alone. By simultaneously considering all three perspectives, the major issues involved in improving power efficiency in light of performance and area constraints are identified. Solutions for the critical elements of a practical computing system are discussed, including the underlying logic device, associated cache memory, off-chip interconnect, and power delivery system. The IBM Blue Gene system is then presented as a case study to exemplify several proposed directions. Going forward, further power reduction may demand radical changes in device technologies and computer architecture; hence, a few such promising methods are briefly considered.
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