Publications

With the recent design shift towards increasing the number of processing elements in a chip, high-bandwidth support in on-chip interconnect is essential for low-latency communication. Much of the previous work has focused on router architectures and network topologies using wide/long channels. However, such solutions may result in a complicated router design and a high interconnect cost. In this paper, we exploit a table-based data compression technique, relying on value patterns in cache traffic. Compressing a large packet into a small one can increase the effective bandwidth of routers and links, while saving power due to reduced operations. The main challenges are providing a scalable implementation of tables and minimizing overhead of the compression latency. First, we propose a shared table scheme that needs one encoding and one decoding tables for each processing element, and a management protocol that does not require in-order delivery. Next, we present streamlined encoding that combines flit injection and encoding in a pipeline. Furthermore, data compression can be selectively applied to communication on congested paths only if compression improves performance. Simulation results in a 16-core CMP show that our compression method improves the packet latency by up to 44% with an average of 36% and reduces the network power consumption by 36% on average.

The explosion of data availability and the demand for faster data analysis have led to the emergence of applications exhibiting large memory footprint and low data reuse rate. These workloads, ranging from neural networks to graph processing, expose compute kernels that operate over myriads of data. Significant data movement requirements of these kernels impose heavy stress on modern memory subsystems and communication fabrics. To mitigate the worsening gap between high CPU computation density and deficient memory bandwidth, solutions like memory networks and near-data processing designs are being architected to improve system performance substantially. In this work, we examine the idea of mapping compute kernels to the memory network so as to leverage in-network computing in data-flow style, by means of near-data processing. We propose Active-Routing, an in-network compute architecture that enables computation on the way for near-data processing by exploiting patterns of aggregation over intermediate results of arithmetic operators. The proposed architecture leverages the massive memory-level parallelism and network concurrency to optimize the aggregation operations along a dynamically built Active-Routing Tree. Our evaluations show that ActiveRouting can achieve upto 7× speedup with an average of 60% performance improvement, and reduce the energy-delay product by 80% across various benchmarks compared to the state-of-the-art processing-in-memory architecture.

General Purpose Graphics Processing Units (GPGPUs) are becoming a cost-effective hardware approach for parallel computing. Many executions on the GPGPUs place heavy stress on the memory system, creating network bottlenecks near memory controllers. We observe that data redundancy in communication traffic is commonplace across a wide range of GPGPU applications. To exploit the data redundancy, we propose a packet coalescing mechanism to alleviate the network bottlenecks by directly reducing the traffic volume. The key idea is to coalesce multiple packets into one without increasing the packet size when they carry redundant cache blocks. To ensure that the coalesced packets are delivered to their respective destinations, we adopt multicast routing for the interconnection network of GPGPUs. Our coalescing approach yields 15% IPC improvement (up to 112%) in a large-scale GPGPU with 2D mesh across various GPGPU applications, by reducing average memory access time (AMAT) by 15.5% (up to 65.2%) and obtaining network bandwidth savings by 13% (up to 37%). Also, our coalescing approach achieves 7% IPC improvement in the NVIDIA Fermi architecture with the crossbar.

The trend of unsustainable power consumption and large memory bandwidth demands in massively parallel multicore systems, with the advent of the big data era, has brought upon the onset of alternate computation paradigms utilizing heterogeneity, specialization, processor-in-memory and approximation. Approximate Computing is being touted as a viable solution for high performance computation by relaxing the accuracy constraints of applications. This trend has been accentuated by emerging data intensive applications in domains like image/video processing, machine learning and big data analytics that allow inaccurate outputs within an acceptable variance. Leveraging relaxed accuracy for high throughput in Networks-onChip (NoCs), which have rapidly become the accepted method for connecting a large number of on-chip components, has not yet been explored. We propose APPROX-NoC, a hardware data approximation framework with an online data error control mechanism for high performance NoCs. APPROX-NoC facilitates approximate matching of data patterns, within a controllable value range, to compress them thereby reducing the volume of data movement across the chip. Our evaluation shows that APPROX-NoC achieves on average up to 9% latency reduction and 60% throughput improvement compared with state-of-the-art NoC data compression mechanisms, while maintaining low application error. Additionally, with a data intensive graph processing application we achieve a 36.7% latency reduction compared to state-of-the-art compression mechanisms

Scalable Networks-on-Chip (NoCs) have become the de facto interconnection mechanism in large scale Chip Multiprocessors. Not only are NoCs devouring a large fraction of the on-chip power budget but static NoC power consumption is becoming the dominant component as technology scales down. Hence reducing static NoC power consumption is critical for energy-efficient computing. Previous research has proposed to power-gate routers attached to inactive cores so as to save static power, but requires centralized control and global network knowledge. In this paper, we propose FlyOver (FLOV), a light-weight distributed mechanism for powergating routers, which encompasses FLOV router architecture, handshake protocols, and a partition-based dynamic routing algorithm to maintain network functionalities. With simple modifications to the baseline router architecture, FLOV can facilitate FLOV links over power-gated routers. Then we present two handshake protocols for FLOV routers, restricted FLOV that can power-gate routers under restricted conditions and generalized FLOV with more power saving capability. The proposed routing algorithm provides best-effort minimal path routing without the necessity for global network information. We evaluate our schemes using synthetic workloads as well as real workloads from PARSEC 2.1 benchmark suite. Our full system evaluations show that FLOV reduces the total and static energy consumption by 18% and 22% respectively, on average across several benchmarks, compared to stateof-the-art NoC power-gating mechanism while keeping the performance degradation minimal.

The communication bandwidth and power consumption of network-on-chip (NoC) are going to meet their limits soon because of traditional metallic interconnects. Photonic NoC (PNoC) is emerging as a promising alternative to address these bottlenecks. However, PNoCs are highly susceptible to thermal fluctuations which is highly common in a manycore chip. This paper first introduces a low power, low cost mesh-based PNoC architecture and provides a quantitative analysis of it’s power consumption over varying on-chip temperature. The paper then proposes a proportional-integral-derivative (PID) heater mechanism that minimizes the effect of thermal variation on PNoC’s performance and power. Experimental results for a 8 × 8 PNoC shows that the proposed technique offers the maximum network-bandwidth considering the thermal effects. Compared to the recently reported results, the proposed design consumes 40% less power and has a temperature variation as low as 1 ◦C.