With the advent of chip multi-processors (CMPs), on-chip networks are critical for providing low-power communications that scale to high core counts. With this motivation, we present a 64-bit, 8x8 mesh Network-on-Chip in 90nm CMOS that: a) bypasses flit buffering in routers using Token Flow Control, thereby reducing buffer power along the control path, and b) uses low-voltage-swing crossbars and links to reduce interconnect energy in the data path. These approaches enable 38% power savings and 39% latency reduction, when compared with an equivalent baseline network. An experimental 2x2 core prototype, operating at 400 MHz, validates our design.
Abstract-A 64-bit, 8 × 8 mesh network-on-chip (NoC) is presented that uses both new architectural and circuit design techniques to improve on-chip network energy-efficiency, latency, and throughput. First, we propose token flow control, which enables bypassing of flit buffering in routers, thereby reducing buffer size and their power consumption. We also incorporate reduced-swing signaling in on-chip links and crossbars to minimize datapath interconnect energy. The 64-node NoC is experimentally validated with a 2 × 2 test chip in 90 nm, 1.2 V CMOS that incorporates traffic generators to emulate the traffic of the full network. Compared with a fully synthesized baseline 8 × 8 NoC architecture designed to meet the same peak throughput, the fabricated prototype reduces network latency by 20% under uniform random traffic, when both networks are run at their maximum operating frequencies. When operated at the same frequencies, the SWIFT NoC reduces network power by 38% and 25% at saturation and low loads, respectively.
Due to their potential capabilities, cognitive radios have recently been creating a significant interest for researchers to develop new techniques that take advantage of their use to increase spectrum utilization. Albeit generally these researchers proposed solutions that address issues related to cognitive radios, they mainly relied on either analytical or simulation tools to evaluate their proposed approaches. Being able to implement and evaluate newlydeveloped techniques in a testbed can be far more effective and accurate than simulation/analytical methods when assessing the performance of such techniques. In this paper, we describe an experimental wireless mesh network that we designed and built from off-the-shelf commercial components at Oregon State University. Our research group is currently developing cross-layer techniques and protocols for nextgeneration cognitive radio networks, and will be using the testbed to implement, test, and evaluate these techniques.
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