The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
It is well known that ASICs have orders of magnitude higher power efficiency than general propose processors. However, due to the high engineering and manufacturing cost only handful of companies can afford to design ASICs. To reduce this cost numerous high-level synthesis tools have emerged since last 2-3 decades. In spite of these tools, ASIC design is still considered expensive because they fail...
The increasing demand for higher resolution of images and communication bandwidth requires the streaming applications to deal with ever increasing size of datasets. Further, with technology scaling the cost of moving data is reducing at a slower pace compared to the cost of computing. These trends have motivated the proposed micro-architectural reorganization of stream processors by dividing the stream...
In spite of decades of research, only a small percentage of hardware is designed using high-level synthesis because of the large gap between the abstraction levels of standard cells and algorithmic level. We propose a grid-based regular physical design platform composed of large grain hardened building blocks called SiLago blocks. This platform is divided into regions which are specialized for different...
This paper presents a hardware based solution for a scalable runtime address generation scheme for DSP applications mapped to a parallel distributed coarse grain reconfigurable computation and storage fabric. The scheme can also deal with non-affine functions of multiple variables that typically correspond to multiple nested loops. The key innovation is the judicious use of two categories of address...
Mapping algorithms on CGRAs can lead to an inefficient implementation and hardware under-utilization if there is a mismatch between the granularity of reconfigurable processing unit and the algorithm. In this paper, we introduce a tool that takes the hardware configuration of a set of applications, identifies the unused parts of the CGRA, and let the user sweep the design space from fully programmable...
A multi-chip custom digital super-computer called eBrain for simulating Bayesian Confidence Propagation Neural Network (BCPNN) model of the human brain has been proposed. It uses Hybrid Memory Cube (HMC), the 3D stacked DRAM memories for storing synaptic weights that are integrated with a custom designed logic chip that implements the BCPNN model. In 22nm node, eBrain executes BCPNN in real time with...
We estimate the computational capacity required to simulate in real time the neural information processing in the human brain. We show that the computational demands of a detailed implementation are beyond reach of current technology, but that some biologically plausible reductions of problem complexity can give performance gains between two and six orders of magnitude, which put implementations within...
This paper presents hardware solution for runtime computation of loop constraints and synchronizing delays for multiple inner loops in parallel distributed implementation of digital signal processing sub-systems. Methods to map and generate the runtime computation code for loop constraints and synchronizing delays are also presented. Compared to the traditional methods, the proposed solution achieves...
SYLVA is a system level synthesis framework that transforms DSP sub-systems modeled as synchronous data flow into hardware implementations in ASIC, FPGAs or CGRAs. SYLVA synthesizes in terms of pre-characterized function implementations (FTMPs). It explores the design space in three dimensions, number of FTMPs, type of FTMPs and pipeline parallelism between the producing and consuming FTMPs. We introduce...
In this paper, we introduce BRIC, a novel custom multi-chip digital computer architecture for simulating in realtime a model of human brain in form of a spiking Bayesian Confidence Propagation Neural Network (BCPNN). The design is conceptually dimensioned for available technology in 2015–2020 with the estimated size of a pizza box, consuming less than 3 kWs of power, delivering 800 Teraflops/sec (single...
This paper presents an industrial case study of using a Coarse Grain Reconfigurable Architecture (CGRA) for a multi-mode accelerator for two kernels: FFT for the LTE standard and the Correlation Pool for the UMTS standard to be executed in a mutually exclusive manner. The CGRA multi-mode accelerator achieved computational efficiency of 39.94 GOPS/watt (OP is multiply-add) and silicon efficiency of...
SYLVA is a System Level Architectural Synthesis Framework that translates Synchronous Data Flow (SDF) models of DSP sub-systems like modems and codecs into hardware implementation in ASIC/Standard Cells, FPGAs or CGRAs (Coarse Grain Reconfigurable Fabric).
This paper presents a self adaptive architecture to enhance the energy efficiency of coarse-grained reconfigurable architectures (CGRAs). Today, platforms host multiple applications, with arbitrary inter-application communication and concurrency patterns. Each application itself can have multiple versions (implementations with different degree of parallelism) and the optimal version can only be determined...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.